Forecasting: Principles and Practice
Hyndman, R.J., & Athanasopoulos, G. (2021) Forecasting: principles and practice, 3rd edition, OTexts: Melbourne, Australia. OTexts.com/fpp3. Accessed on 2/26/22
library(fpp3)
library(fable.prophet)## Loading required package: RcppPreface
library(fpp3)
ggplot2::theme_set(theme_minimal())Forecasting: Principles and Practice Forecasting: Principles and Practice
library(fpp3)Preface Preface
library(fpp3)2.1 tsibble objects 2.1 tsibble objects
olympic_running## # A tsibble: 312 x 4 [4Y]
## # Key: Length, Sex [14]
## Year Length Sex Time
## <int> <int> <chr> <dbl>
## 1 1896 100 men 12
## 2 1900 100 men 11
## 3 1904 100 men 11
## 4 1908 100 men 10.8
## 5 1912 100 men 10.8
## 6 1916 100 men NA
## 7 1920 100 men 10.8
## 8 1924 100 men 10.6
## 9 1928 100 men 10.8
## 10 1932 100 men 10.3
## # … with 302 more rowsolympic_running %>% distinct(Sex)## # A tibble: 2 × 1
## Sex
## <chr>
## 1 men
## 2 womenPBS## # A tsibble: 67,596 x 9 [1M]
## # Key: Concession, Type, ATC1, ATC2 [336]
## Month Concession Type ATC1 ATC1_desc ATC2 ATC2_desc Scripts Cost
## <mth> <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl>
## 1 1991 Jul Concessional Co-payme… A Alimenta… A01 STOMATOL… 18228 67877
## 2 1991 Aug Concessional Co-payme… A Alimenta… A01 STOMATOL… 15327 57011
## 3 1991 Sep Concessional Co-payme… A Alimenta… A01 STOMATOL… 14775 55020
## 4 1991 Oct Concessional Co-payme… A Alimenta… A01 STOMATOL… 15380 57222
## 5 1991 Nov Concessional Co-payme… A Alimenta… A01 STOMATOL… 14371 52120
## 6 1991 Dec Concessional Co-payme… A Alimenta… A01 STOMATOL… 15028 54299
## 7 1992 Jan Concessional Co-payme… A Alimenta… A01 STOMATOL… 11040 39753
## 8 1992 Feb Concessional Co-payme… A Alimenta… A01 STOMATOL… 15165 54405
## 9 1992 Mar Concessional Co-payme… A Alimenta… A01 STOMATOL… 16898 61108
## 10 1992 Apr Concessional Co-payme… A Alimenta… A01 STOMATOL… 18141 65356
## # … with 67,586 more rowsPBS %>%
filter(ATC2 == "A10")## # A tsibble: 816 x 9 [1M]
## # Key: Concession, Type, ATC1, ATC2 [4]
## Month Concession Type ATC1 ATC1_desc ATC2 ATC2_desc Scripts Cost
## <mth> <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl>
## 1 1991 Jul Concessional Co-paym… A Alimenta… A10 ANTIDIAB… 89733 2.09e6
## 2 1991 Aug Concessional Co-paym… A Alimenta… A10 ANTIDIAB… 77101 1.80e6
## 3 1991 Sep Concessional Co-paym… A Alimenta… A10 ANTIDIAB… 76255 1.78e6
## 4 1991 Oct Concessional Co-paym… A Alimenta… A10 ANTIDIAB… 78681 1.85e6
## 5 1991 Nov Concessional Co-paym… A Alimenta… A10 ANTIDIAB… 70554 1.69e6
## 6 1991 Dec Concessional Co-paym… A Alimenta… A10 ANTIDIAB… 75814 1.84e6
## 7 1992 Jan Concessional Co-paym… A Alimenta… A10 ANTIDIAB… 64186 1.56e6
## 8 1992 Feb Concessional Co-paym… A Alimenta… A10 ANTIDIAB… 75899 1.73e6
## 9 1992 Mar Concessional Co-paym… A Alimenta… A10 ANTIDIAB… 89445 2.05e6
## 10 1992 Apr Concessional Co-paym… A Alimenta… A10 ANTIDIAB… 97315 2.23e6
## # … with 806 more rowsPBS %>%
filter(ATC2 == "A10") %>%
select(Month, Concession, Type, Cost)## # A tsibble: 816 x 4 [1M]
## # Key: Concession, Type [4]
## Month Concession Type Cost
## <mth> <chr> <chr> <dbl>
## 1 1991 Jul Concessional Co-payments 2092878
## 2 1991 Aug Concessional Co-payments 1795733
## 3 1991 Sep Concessional Co-payments 1777231
## 4 1991 Oct Concessional Co-payments 1848507
## 5 1991 Nov Concessional Co-payments 1686458
## 6 1991 Dec Concessional Co-payments 1843079
## 7 1992 Jan Concessional Co-payments 1564702
## 8 1992 Feb Concessional Co-payments 1732508
## 9 1992 Mar Concessional Co-payments 2046102
## 10 1992 Apr Concessional Co-payments 2225977
## # … with 806 more rowsPBS %>%
filter(ATC2 == "A10") %>%
select(Month, Concession, Type, Cost) %>%
summarise(TotalC = sum(Cost))## # A tsibble: 204 x 2 [1M]
## Month TotalC
## <mth> <dbl>
## 1 1991 Jul 3526591
## 2 1991 Aug 3180891
## 3 1991 Sep 3252221
## 4 1991 Oct 3611003
## 5 1991 Nov 3565869
## 6 1991 Dec 4306371
## 7 1992 Jan 5088335
## 8 1992 Feb 2814520
## 9 1992 Mar 2985811
## 10 1992 Apr 3204780
## # … with 194 more rowsPBS %>%
filter(ATC2 == "A10") %>%
select(Month, Concession, Type, Cost) %>%
summarise(TotalC = sum(Cost)) %>%
mutate(Cost = TotalC/1e6)## # A tsibble: 204 x 3 [1M]
## Month TotalC Cost
## <mth> <dbl> <dbl>
## 1 1991 Jul 3526591 3.53
## 2 1991 Aug 3180891 3.18
## 3 1991 Sep 3252221 3.25
## 4 1991 Oct 3611003 3.61
## 5 1991 Nov 3565869 3.57
## 6 1991 Dec 4306371 4.31
## 7 1992 Jan 5088335 5.09
## 8 1992 Feb 2814520 2.81
## 9 1992 Mar 2985811 2.99
## 10 1992 Apr 3204780 3.20
## # … with 194 more rowsPBS %>%
filter(ATC2 == "A10") %>%
select(Month, Concession, Type, Cost) %>%
summarise(TotalC = sum(Cost)) %>%
mutate(Cost = TotalC / 1e6) -> a10
prison <- readr::read_csv("https://OTexts.com/fpp3/extrafiles/prison_population.csv")## Rows: 3072 Columns: 6
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (4): State, Gender, Legal, Indigenous
## dbl (1): Count
## date (1): Date
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.prison <- prison %>%
mutate(Quarter = yearquarter(Date)) %>%
select(-Date) %>%
as_tsibble(key = c(State, Gender, Legal, Indigenous),
index = Quarter)
prison## # A tsibble: 3,072 x 6 [1Q]
## # Key: State, Gender, Legal, Indigenous [64]
## State Gender Legal Indigenous Count Quarter
## <chr> <chr> <chr> <chr> <dbl> <qtr>
## 1 ACT Female Remanded ATSI 0 2005 Q1
## 2 ACT Female Remanded ATSI 1 2005 Q2
## 3 ACT Female Remanded ATSI 0 2005 Q3
## 4 ACT Female Remanded ATSI 0 2005 Q4
## 5 ACT Female Remanded ATSI 1 2006 Q1
## 6 ACT Female Remanded ATSI 1 2006 Q2
## 7 ACT Female Remanded ATSI 1 2006 Q3
## 8 ACT Female Remanded ATSI 0 2006 Q4
## 9 ACT Female Remanded ATSI 0 2007 Q1
## 10 ACT Female Remanded ATSI 1 2007 Q2
## # … with 3,062 more rows2.2 Time plots 2.2 Time plots
melsyd_economy <- ansett %>%
filter(Airports == "MEL-SYD", Class == "Economy") %>%
mutate(Passengers = Passengers/1000)
autoplot(melsyd_economy, Passengers) +
labs(title = "Ansett airlines economy class",
subtitle = "Melbourne-Sydney",
y = "Passengers ('000)")
autoplot(a10, Cost) +
labs(y = "$ (millions)",
title = "Australian antidiabetic drug sales")
2.4 Seasonal plots 2.4 Seasonal plots
a10 %>%
gg_season(Cost, labels = "both") +
labs(y = "$ (millions)",
title = "Seasonal plot: Antidiabetic drug sales")
vic_elec %>% gg_season(Demand, period = "day") +
theme(legend.position = "none") +
labs(y="MWh", title="Electricity demand: Victoria")
vic_elec %>% gg_season(Demand, period = "week") +
theme(legend.position = "none") +
labs(y="MWh", title="Electricity demand: Victoria")
vic_elec %>% gg_season(Demand, period = "year") +
labs(y="MWh", title="Electricity demand: Victoria")
# 2.5 Seasonal subseries plots
2.5 Seasonal subseries plots
a10 %>%
gg_subseries(Cost) +
labs(
y = "$ (millions)",
title = "Australian antidiabetic drug sales"
)
holidays <- tourism %>%
filter(Purpose == "Holiday") %>%
group_by(State) %>%
summarise(Trips = sum(Trips))
holidays## # A tsibble: 640 x 3 [1Q]
## # Key: State [8]
## State Quarter Trips
## <chr> <qtr> <dbl>
## 1 ACT 1998 Q1 196.
## 2 ACT 1998 Q2 127.
## 3 ACT 1998 Q3 111.
## 4 ACT 1998 Q4 170.
## 5 ACT 1999 Q1 108.
## 6 ACT 1999 Q2 125.
## 7 ACT 1999 Q3 178.
## 8 ACT 1999 Q4 218.
## 9 ACT 2000 Q1 158.
## 10 ACT 2000 Q2 155.
## # … with 630 more rowsautoplot(holidays, Trips) +
labs(y = "Overnight trips ('000)",
title = "Australian domestic holidays")
gg_season(holidays, Trips) +
labs(y = "Overnight trips ('000)",
title = "Australian domestic holidays")
holidays %>%
gg_subseries(Trips) +
labs(y = "Overnight trips ('000)",
title = "Australian domestic holidays")
# 2.6 Scatterplots
2.6 Scatterplots
vic_elec %>%
filter(year(Time) == 2014) %>%
autoplot(Demand) +
labs(y = "GW",
title = "Half-hourly electricity demand: Victoria")
vic_elec %>%
filter(year(Time) == 2014) %>%
autoplot(Temperature) +
labs(
y = "Degrees Celsius",
title = "Half-hourly temperatures: Melbourne, Australia"
)
vic_elec %>%
filter(year(Time) == 2014) %>%
ggplot(aes(x = Temperature, y = Demand)) +
geom_point() +
labs(x = "Temperature (degrees Celsius)",
y = "Electricity demand (GW)")
visitors <- tourism %>%
group_by(State) %>%
summarise(Trips = sum(Trips))
visitors %>%
ggplot(aes(x = Quarter, y = Trips)) +
geom_line() +
facet_grid(vars(State), scales = "free_y") +
labs(title = "Australian domestic tourism",
y= "Overnight trips ('000)")
visitors %>%
pivot_wider(values_from=Trips, names_from=State) %>%
GGally::ggpairs(columns = 2:9)## Registered S3 method overwritten by 'GGally':
## method from
## +.gg ggplot2
# 2.7 Lag plots
2.7 Lag plots
recent_production <- aus_production %>%
filter(year(Quarter) >= 2000)
recent_production %>%
gg_lag(Beer, geom = "point") +
labs(x = "lag(Beer, k)")
# 2.8 Autocorrelation
2.8 Autocorrelation
recent_production %>% ACF(Beer, lag_max = 9)## # A tsibble: 9 x 2 [1Q]
## lag acf
## <lag> <dbl>
## 1 1Q -0.0530
## 2 2Q -0.758
## 3 3Q -0.0262
## 4 4Q 0.802
## 5 5Q -0.0775
## 6 6Q -0.657
## 7 7Q 0.00119
## 8 8Q 0.707
## 9 9Q -0.0888recent_production %>%
ACF(Beer) %>%
autoplot() + labs(title="Australian beer production")
a10 %>%
ACF(Cost, lag_max = 48) %>%
autoplot() +
labs(title="Australian antidiabetic drug sales")
# 2.9 White noise
2.9 White noise
set.seed(30)
y <- tsibble(sample = 1:50, wn = rnorm(50), index = sample)
y %>% autoplot(wn) + labs(title = "White noise", y = "")
y %>%
ACF(wn) %>%
autoplot() + labs(title = "White noise")
# 2.10 Exercises
2.10 Exercises
set.seed(12345678)
myseries <- aus_retail %>%
filter(`Series ID` == sample(aus_retail$`Series ID`,1))
dgoog <- gafa_stock %>%
filter(Symbol == "GOOG", year(Date) >= 2018) %>%
mutate(trading_day = row_number()) %>%
update_tsibble(index = trading_day, regular = TRUE) %>%
mutate(diff = difference(Close))3.1 Transformations and adjustments 3.1 Transformations and adjustments
global_economy %>%
filter(Country == "Australia") %>%
autoplot(GDP/Population) +
labs(title= "GDP per capita", y = "$US")
print_retail <- aus_retail %>%
filter(Industry == "Newspaper and book retailing") %>%
group_by(Industry) %>%
index_by(Year = year(Month)) %>%
summarise(Turnover = sum(Turnover))
aus_economy <- global_economy %>%
filter(Code == "AUS")
print_retail %>%
left_join(aus_economy, by = "Year") %>%
mutate(Adjusted_turnover = Turnover / CPI * 100) %>%
pivot_longer(c(Turnover, Adjusted_turnover),
values_to = "Turnover") %>%
mutate(name = factor(name,
levels=c("Turnover","Adjusted_turnover"))) %>%
ggplot(aes(x = Year, y = Turnover)) +
geom_line() +
facet_grid(name ~ ., scales = "free_y") +
labs(title = "Turnover: Australian print media industry",
y = "$AU")## Warning: Removed 1 row(s) containing missing values (geom_path).
lambda <- aus_production %>%
features(Gas, features = guerrero) %>%
pull(lambda_guerrero)
aus_production %>%
autoplot(box_cox(Gas, lambda)) +
labs(y = "",
title = latex2exp::TeX(paste0(
"Transformed gas production with $\\lambda$ = ",
round(lambda,2))))
# 3.2 Time series components
3.2 Time series components
us_retail_employment <- us_employment %>%
filter(year(Month) >= 1990, Title == "Retail Trade") %>%
select(-Series_ID)
autoplot(us_retail_employment, Employed) +
labs(y = "Persons (thousands)",
title = "Total employment in US retail")
dcmp <- us_retail_employment %>%
model(stl = STL(Employed))
components(dcmp)## # A dable: 357 x 7 [1M]
## # Key: .model [1]
## # : Employed = trend + season_year + remainder
## .model Month Employed trend season_year remainder season_adjust
## <chr> <mth> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 stl 1990 Jan 13256. 13288. -33.0 0.836 13289.
## 2 stl 1990 Feb 12966. 13269. -258. -44.6 13224.
## 3 stl 1990 Mar 12938. 13250. -290. -22.1 13228.
## 4 stl 1990 Apr 13012. 13231. -220. 1.05 13232.
## 5 stl 1990 May 13108. 13211. -114. 11.3 13223.
## 6 stl 1990 Jun 13183. 13192. -24.3 15.5 13207.
## 7 stl 1990 Jul 13170. 13172. -23.2 21.6 13193.
## 8 stl 1990 Aug 13160. 13151. -9.52 17.8 13169.
## 9 stl 1990 Sep 13113. 13131. -39.5 22.0 13153.
## 10 stl 1990 Oct 13185. 13110. 61.6 13.2 13124.
## # … with 347 more rowscomponents(dcmp) %>%
as_tsibble() %>%
autoplot(Employed, colour="gray") +
geom_line(aes(y=trend), colour = "#D55E00") +
labs(
y = "Persons (thousands)",
title = "Total employment in US retail"
)
components(dcmp) %>% autoplot()
components(dcmp) %>%
as_tsibble() %>%
autoplot(Employed, colour = "gray") +
geom_line(aes(y=season_adjust), colour = "#0072B2") +
labs(y = "Persons (thousands)",
title = "Total employment in US retail")
# 3.3 Moving averages
3.3 Moving averages
global_economy %>%
filter(Country == "Australia") %>%
autoplot(Exports) +
labs(y = "% of GDP", title = "Total Australian exports")
aus_exports <- global_economy %>%
filter(Country == "Australia") %>%
mutate(
`5-MA` = slider::slide_dbl(Exports, mean,
.before = 2, .after = 2, .complete = TRUE)
)
aus_exports %>%
autoplot(Exports) +
geom_line(aes(y = `5-MA`), colour = "#D55E00") +
labs(y = "% of GDP",
title = "Total Australian exports") +
guides(colour = guide_legend(title = "series"))## Warning: Removed 4 row(s) containing missing values (geom_path).
beer <- aus_production %>%
filter(year(Quarter) >= 1992) %>%
select(Quarter, Beer)
beer_ma <- beer %>%
mutate(
`4-MA` = slider::slide_dbl(Beer, mean,
.before = 1, .after = 2, .complete = TRUE),
`2x4-MA` = slider::slide_dbl(`4-MA`, mean,
.before = 1, .after = 0, .complete = TRUE)
)
us_retail_employment_ma <- us_retail_employment %>%
mutate(
`12-MA` = slider::slide_dbl(Employed, mean,
.before = 5, .after = 6, .complete = TRUE),
`2x12-MA` = slider::slide_dbl(`12-MA`, mean,
.before = 1, .after = 0, .complete = TRUE)
)
us_retail_employment_ma %>%
autoplot(Employed, colour = "gray") +
geom_line(aes(y = `2x12-MA`), colour = "#D55E00") +
labs(y = "Persons (thousands)",
title = "Total employment in US retail")## Warning: Removed 12 row(s) containing missing values (geom_path).
# 3.4 Classical decomposition
3.4 Classical decomposition
us_retail_employment %>%
model(
classical_decomposition(Employed, type = "additive")
) %>%
components() %>%
autoplot() +
labs(title = "Classical additive decomposition of total
US retail employment")## Warning: Removed 6 row(s) containing missing values (geom_path).
# 3.5 Methods used by official statistics agencies
3.5 Methods used by official statistics agencies
x11_dcmp <- us_retail_employment %>%
model(x11 = X_13ARIMA_SEATS(Employed ~ x11())) %>%
components()
autoplot(x11_dcmp) +
labs(title =
"Decomposition of total US retail employment using X-11.")
x11_dcmp %>%
ggplot(aes(x = Month)) +
geom_line(aes(y = Employed, colour = "Data")) +
geom_line(aes(y = season_adjust,
colour = "Seasonally Adjusted")) +
geom_line(aes(y = trend, colour = "Trend")) +
labs(y = "Persons (thousands)",
title = "Total employment in US retail") +
scale_colour_manual(
values = c("gray", "#0072B2", "#D55E00"),
breaks = c("Data", "Seasonally Adjusted", "Trend")
)
x11_dcmp %>%
gg_subseries(seasonal)
seats_dcmp <- us_retail_employment %>%
model(seats = X_13ARIMA_SEATS(Employed ~ seats())) %>%
components()
autoplot(seats_dcmp) +
labs(title =
"Decomposition of total US retail employment using SEATS")
# 3.6 STL decomposition
3.6 STL decomposition
us_retail_employment %>%
model(
STL(Employed ~ trend(window = 7) +
season(window = "periodic"),
robust = TRUE)) %>%
components() %>%
autoplot()
# 3.7 Exercises
3.7 Exercises
gas <- tail(aus_production, 5*4) %>% select(Gas)4.1 Some simple statistics 4.1 Some simple statistics
tourism %>%
features(Trips, list(mean = mean)) %>%
arrange(mean)## # A tibble: 304 × 4
## Region State Purpose mean
## <chr> <chr> <chr> <dbl>
## 1 Kangaroo Island South Australia Other 0.340
## 2 MacDonnell Northern Territory Other 0.449
## 3 Wilderness West Tasmania Other 0.478
## 4 Barkly Northern Territory Other 0.632
## 5 Clare Valley South Australia Other 0.898
## 6 Barossa South Australia Other 1.02
## 7 Kakadu Arnhem Northern Territory Other 1.04
## 8 Lasseter Northern Territory Other 1.14
## 9 Wimmera Victoria Other 1.15
## 10 MacDonnell Northern Territory Visiting 1.18
## # … with 294 more rowstourism %>% features(Trips, quantile)## # A tibble: 304 × 8
## Region State Purpose `0%` `25%` `50%` `75%` `100%`
## <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Adelaide South Australia Busine… 68.7 134. 153. 177. 242.
## 2 Adelaide South Australia Holiday 108. 135. 154. 172. 224.
## 3 Adelaide South Australia Other 25.9 43.9 53.8 62.5 107.
## 4 Adelaide South Australia Visiti… 137. 179. 206. 229. 270.
## 5 Adelaide Hills South Australia Busine… 0 0 1.26 3.92 28.6
## 6 Adelaide Hills South Australia Holiday 0 5.77 8.52 14.1 35.8
## 7 Adelaide Hills South Australia Other 0 0 0.908 2.09 8.95
## 8 Adelaide Hills South Australia Visiti… 0.778 8.91 12.2 16.8 81.1
## 9 Alice Springs Northern Territo… Busine… 1.01 9.13 13.3 18.5 34.1
## 10 Alice Springs Northern Territo… Holiday 2.81 16.9 31.5 44.8 76.5
## # … with 294 more rows4.2 ACF features 4.2 ACF features
tourism %>% features(Trips, feat_acf)## # A tibble: 304 × 10
## Region State Purpose acf1 acf10 diff1_acf1 diff1_acf10 diff2_acf1
## <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Adelaide Sout… Busine… 0.0333 0.131 -0.520 0.463 -0.676
## 2 Adelaide Sout… Holiday 0.0456 0.372 -0.343 0.614 -0.487
## 3 Adelaide Sout… Other 0.517 1.15 -0.409 0.383 -0.675
## 4 Adelaide Sout… Visiti… 0.0684 0.294 -0.394 0.452 -0.518
## 5 Adelaide Hills Sout… Busine… 0.0709 0.134 -0.580 0.415 -0.750
## 6 Adelaide Hills Sout… Holiday 0.131 0.313 -0.536 0.500 -0.716
## 7 Adelaide Hills Sout… Other 0.261 0.330 -0.253 0.317 -0.457
## 8 Adelaide Hills Sout… Visiti… 0.139 0.117 -0.472 0.239 -0.626
## 9 Alice Springs Nort… Busine… 0.217 0.367 -0.500 0.381 -0.658
## 10 Alice Springs Nort… Holiday -0.00660 2.11 -0.153 2.11 -0.274
## # … with 294 more rows, and 2 more variables: diff2_acf10 <dbl>,
## # season_acf1 <dbl>4.3 STL Features 4.3 STL Features
tourism %>%
features(Trips, feat_stl)## # A tibble: 304 × 12
## Region State Purpose trend_strength seasonal_streng… seasonal_peak_y…
## <chr> <chr> <chr> <dbl> <dbl> <dbl>
## 1 Adelaide Sout… Busine… 0.464 0.407 3
## 2 Adelaide Sout… Holiday 0.554 0.619 1
## 3 Adelaide Sout… Other 0.746 0.202 2
## 4 Adelaide Sout… Visiti… 0.435 0.452 1
## 5 Adelaide Hills Sout… Busine… 0.464 0.179 3
## 6 Adelaide Hills Sout… Holiday 0.528 0.296 2
## 7 Adelaide Hills Sout… Other 0.593 0.404 2
## 8 Adelaide Hills Sout… Visiti… 0.488 0.254 0
## 9 Alice Springs Nort… Busine… 0.534 0.251 0
## 10 Alice Springs Nort… Holiday 0.381 0.832 3
## # … with 294 more rows, and 6 more variables: seasonal_trough_year <dbl>,
## # spikiness <dbl>, linearity <dbl>, curvature <dbl>, stl_e_acf1 <dbl>,
## # stl_e_acf10 <dbl>tourism %>%
features(Trips, feat_stl) %>%
ggplot(aes(x = trend_strength, y = seasonal_strength_year,
col = Purpose)) +
geom_point() +
facet_wrap(vars(State))
tourism %>%
features(Trips, feat_stl) %>%
filter(
seasonal_strength_year == max(seasonal_strength_year)
) %>%
left_join(tourism, by = c("State", "Region", "Purpose")) %>%
ggplot(aes(x = Quarter, y = Trips)) +
geom_line() +
facet_grid(vars(State, Region, Purpose))
4.5 Exploring Australian tourism data 4.5 Exploring Australian tourism data
tourism_features <- tourism %>%
features(Trips, feature_set(pkgs = "feasts"))## Warning: `n_flat_spots()` was deprecated in feasts 0.1.5.
## Please use `longest_flat_spot()` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was generated.tourism_features## # A tibble: 304 × 51
## Region State Purpose trend_strength seasonal_streng… seasonal_peak_y…
## <chr> <chr> <chr> <dbl> <dbl> <dbl>
## 1 Adelaide Sout… Busine… 0.464 0.407 3
## 2 Adelaide Sout… Holiday 0.554 0.619 1
## 3 Adelaide Sout… Other 0.746 0.202 2
## 4 Adelaide Sout… Visiti… 0.435 0.452 1
## 5 Adelaide Hills Sout… Busine… 0.464 0.179 3
## 6 Adelaide Hills Sout… Holiday 0.528 0.296 2
## 7 Adelaide Hills Sout… Other 0.593 0.404 2
## 8 Adelaide Hills Sout… Visiti… 0.488 0.254 0
## 9 Alice Springs Nort… Busine… 0.534 0.251 0
## 10 Alice Springs Nort… Holiday 0.381 0.832 3
## # … with 294 more rows, and 45 more variables: seasonal_trough_year <dbl>,
## # spikiness <dbl>, linearity <dbl>, curvature <dbl>, stl_e_acf1 <dbl>,
## # stl_e_acf10 <dbl>, acf1 <dbl>, acf10 <dbl>, diff1_acf1 <dbl>,
## # diff1_acf10 <dbl>, diff2_acf1 <dbl>, diff2_acf10 <dbl>, season_acf1 <dbl>,
## # pacf5 <dbl>, diff1_pacf5 <dbl>, diff2_pacf5 <dbl>, season_pacf <dbl>,
## # zero_run_mean <dbl>, nonzero_squared_cv <dbl>, zero_start_prop <dbl>,
## # zero_end_prop <dbl>, lambda_guerrero <dbl>, kpss_stat <dbl>, …library(glue)
tourism_features %>%
select_at(vars(contains("season"), Purpose)) %>%
mutate(
seasonal_peak_year = seasonal_peak_year +
4*(seasonal_peak_year==0),
seasonal_trough_year = seasonal_trough_year +
4*(seasonal_trough_year==0),
seasonal_peak_year = glue("Q{seasonal_peak_year}"),
seasonal_trough_year = glue("Q{seasonal_trough_year}"),
) %>%
GGally::ggpairs(mapping = aes(colour = Purpose))## Registered S3 method overwritten by 'GGally':
## method from
## +.gg ggplot2## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
library(broom)
pcs <- tourism_features %>%
select(-State, -Region, -Purpose) %>%
prcomp(scale = TRUE) %>%
augment(tourism_features)
pcs %>%
ggplot(aes(x = .fittedPC1, y = .fittedPC2, col = Purpose)) +
geom_point() +
theme(aspect.ratio = 1)
outliers <- pcs %>%
filter(.fittedPC1 > 10) %>%
select(Region, State, Purpose, .fittedPC1, .fittedPC2)
outliers## # A tibble: 4 × 5
## Region State Purpose .fittedPC1 .fittedPC2
## <chr> <chr> <chr> <dbl> <dbl>
## 1 Australia's North West Western Australia Business 13.4 -11.3
## 2 Australia's South West Western Australia Holiday 10.9 0.880
## 3 Melbourne Victoria Holiday 12.3 -10.4
## 4 South Coast New South Wales Holiday 11.9 9.42outliers %>%
left_join(tourism, by = c("State", "Region", "Purpose")) %>%
mutate(
Series = glue("{State}", "{Region}", "{Purpose}",
.sep = "\n\n")
) %>%
ggplot(aes(x = Quarter, y = Trips)) +
geom_line() +
facet_grid(Series ~ ., scales = "free") +
labs(title = "Outlying time series in PC space")
5.1 A tidy forecasting workflow 5.1 A tidy forecasting workflow
gdppc <- global_economy %>%
mutate(GDP_per_capita = GDP / Population)
gdppc %>%
filter(Country == "Sweden") %>%
autoplot(GDP_per_capita) +
labs(y = "$US", title = "GDP per capita for Sweden")
# TSLM(GDP_per_capita ~ trend())
fit <- gdppc %>%
model(trend_model = TSLM(GDP_per_capita ~ trend()))## Warning: 7 errors (1 unique) encountered for trend_model
## [7] 0 (non-NA) casesfit## # A mable: 263 x 2
## # Key: Country [263]
## Country trend_model
## <fct> <model>
## 1 Afghanistan <TSLM>
## 2 Albania <TSLM>
## 3 Algeria <TSLM>
## 4 American Samoa <TSLM>
## 5 Andorra <TSLM>
## 6 Angola <TSLM>
## 7 Antigua and Barbuda <TSLM>
## 8 Arab World <TSLM>
## 9 Argentina <TSLM>
## 10 Armenia <TSLM>
## # … with 253 more rowsfit %>% forecast(h = "3 years")## # A fable: 789 x 5 [1Y]
## # Key: Country, .model [263]
## Country .model Year GDP_per_capita .mean
## <fct> <chr> <dbl> <dist> <dbl>
## 1 Afghanistan trend_model 2018 N(526, 9653) 526.
## 2 Afghanistan trend_model 2019 N(534, 9689) 534.
## 3 Afghanistan trend_model 2020 N(542, 9727) 542.
## 4 Albania trend_model 2018 N(4716, 476419) 4716.
## 5 Albania trend_model 2019 N(4867, 481086) 4867.
## 6 Albania trend_model 2020 N(5018, 486012) 5018.
## 7 Algeria trend_model 2018 N(4410, 643094) 4410.
## 8 Algeria trend_model 2019 N(4489, 645311) 4489.
## 9 Algeria trend_model 2020 N(4568, 647602) 4568.
## 10 American Samoa trend_model 2018 N(12491, 652926) 12491.
## # … with 779 more rowsfit %>%
forecast(h = "3 years") %>%
filter(Country == "Sweden") %>%
autoplot(gdppc) +
labs(y = "$US", title = "GDP per capita for Sweden")
# 5.2 Some simple forecasting methods
5.2 Some simple forecasting methods
bricks <- aus_production %>%
filter_index("1970 Q1" ~ "2004 Q4") %>%
select(Bricks)
bricks %>% model(MEAN(Bricks))## # A mable: 1 x 1
## `MEAN(Bricks)`
## <model>
## 1 <MEAN>bricks %>% model(NAIVE(Bricks))## # A mable: 1 x 1
## `NAIVE(Bricks)`
## <model>
## 1 <NAIVE>bricks %>% model(SNAIVE(Bricks ~ lag("year")))## # A mable: 1 x 1
## `SNAIVE(Bricks ~ lag("year"))`
## <model>
## 1 <SNAIVE>bricks %>% model(RW(Bricks ~ drift()))## # A mable: 1 x 1
## `RW(Bricks ~ drift())`
## <model>
## 1 <RW w/ drift># Set training data from 1992 to 2006
train <- aus_production %>%
filter_index("1992 Q1" ~ "2006 Q4")
# Fit the models
beer_fit <- train %>%
model(
Mean = MEAN(Beer),
`Naïve` = NAIVE(Beer),
`Seasonal naïve` = SNAIVE(Beer)
)
# Generate forecasts for 14 quarters
beer_fc <- beer_fit %>% forecast(h = 14)
# Plot forecasts against actual values
beer_fc %>%
autoplot(train, level = NULL) +
autolayer(
filter_index(aus_production, "2007 Q1" ~ .),
colour = "black"
) +
labs(
y = "Megalitres",
title = "Forecasts for quarterly beer production"
) +
guides(colour = guide_legend(title = "Forecast"))## Plot variable not specified, automatically selected `.vars = Beer`
# Re-index based on trading days
google_stock <- gafa_stock %>%
filter(Symbol == "GOOG", year(Date) >= 2015) %>%
mutate(day = row_number()) %>%
update_tsibble(index = day, regular = TRUE)
# Filter the year of interest
google_2015 <- google_stock %>% filter(year(Date) == 2015)
# Fit the models
google_fit <- google_2015 %>%
model(
Mean = MEAN(Close),
`Naïve` = NAIVE(Close),
Drift = NAIVE(Close ~ drift())
)
# Produce forecasts for the trading days in January 2016
google_jan_2016 <- google_stock %>%
filter(yearmonth(Date) == yearmonth("2016 Jan"))
google_fc <- google_fit %>%
forecast(new_data = google_jan_2016)
# Plot the forecasts
google_fc %>%
autoplot(google_2015, level = NULL) +
autolayer(google_jan_2016, Close, colour = "black") +
labs(y = "$US",
title = "Google daily closing stock prices",
subtitle = "(Jan 2015 - Jan 2016)") +
guides(colour = guide_legend(title = "Forecast"))
# 5.3 Fitted values and residuals
5.3 Fitted values and residuals
augment(beer_fit)## # A tsibble: 180 x 6 [1Q]
## # Key: .model [3]
## .model Quarter Beer .fitted .resid .innov
## <chr> <qtr> <dbl> <dbl> <dbl> <dbl>
## 1 Mean 1992 Q1 443 436. 6.55 6.55
## 2 Mean 1992 Q2 410 436. -26.4 -26.4
## 3 Mean 1992 Q3 420 436. -16.4 -16.4
## 4 Mean 1992 Q4 532 436. 95.6 95.6
## 5 Mean 1993 Q1 433 436. -3.45 -3.45
## 6 Mean 1993 Q2 421 436. -15.4 -15.4
## 7 Mean 1993 Q3 410 436. -26.4 -26.4
## 8 Mean 1993 Q4 512 436. 75.6 75.6
## 9 Mean 1994 Q1 449 436. 12.6 12.6
## 10 Mean 1994 Q2 381 436. -55.4 -55.4
## # … with 170 more rows5.4 Residual diagnostics 5.4 Residual diagnostics
autoplot(google_2015, Close) +
labs(y = "$US",
title = "Google daily closing stock prices in 2015")
aug <- google_2015 %>%
model(NAIVE(Close)) %>%
augment()
autoplot(aug, .innov) +
labs(y = "$US",
title = "Residuals from the naïve method")## Warning: Removed 1 row(s) containing missing values (geom_path).
aug %>%
ggplot(aes(x = .innov)) +
geom_histogram() +
labs(title = "Histogram of residuals")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 1 rows containing non-finite values (stat_bin).
aug %>%
ACF(.innov) %>%
autoplot() +
labs(title = "Residuals from the naïve method")
google_2015 %>%
model(NAIVE(Close)) %>%
gg_tsresiduals()## Warning: Removed 1 row(s) containing missing values (geom_path).## Warning: Removed 1 rows containing missing values (geom_point).## Warning: Removed 1 rows containing non-finite values (stat_bin).
aug %>% features(.innov, box_pierce, lag = 10, dof = 0)## # A tibble: 1 × 4
## Symbol .model bp_stat bp_pvalue
## <chr> <chr> <dbl> <dbl>
## 1 GOOG NAIVE(Close) 7.74 0.654aug %>% features(.innov, ljung_box, lag = 10, dof = 0)## # A tibble: 1 × 4
## Symbol .model lb_stat lb_pvalue
## <chr> <chr> <dbl> <dbl>
## 1 GOOG NAIVE(Close) 7.91 0.637fit <- google_2015 %>% model(RW(Close ~ drift()))
tidy(fit)## # A tibble: 1 × 7
## Symbol .model term estimate std.error statistic p.value
## <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 GOOG RW(Close ~ drift()) b 0.944 0.705 1.34 0.182augment(fit) %>% features(.innov, ljung_box, lag=10, dof=1)## # A tibble: 1 × 4
## Symbol .model lb_stat lb_pvalue
## <chr> <chr> <dbl> <dbl>
## 1 GOOG RW(Close ~ drift()) 7.91 0.5435.5 Distributional forecasts and prediction intervals 5.5 Distributional forecasts and prediction intervals
google_2015 %>%
model(NAIVE(Close)) %>%
forecast(h = 10) %>%
hilo()## # A tsibble: 10 x 7 [1]
## # Key: Symbol, .model [1]
## Symbol .model day Close .mean `80%`
## <chr> <chr> <dbl> <dist> <dbl> <hilo>
## 1 GOOG NAIVE(Close) 253 N(759, 125) 759. [744.5400, 773.2200]80
## 2 GOOG NAIVE(Close) 254 N(759, 250) 759. [738.6001, 779.1599]80
## 3 GOOG NAIVE(Close) 255 N(759, 376) 759. [734.0423, 783.7177]80
## 4 GOOG NAIVE(Close) 256 N(759, 501) 759. [730.1999, 787.5601]80
## 5 GOOG NAIVE(Close) 257 N(759, 626) 759. [726.8147, 790.9453]80
## 6 GOOG NAIVE(Close) 258 N(759, 751) 759. [723.7543, 794.0058]80
## 7 GOOG NAIVE(Close) 259 N(759, 876) 759. [720.9399, 796.8202]80
## 8 GOOG NAIVE(Close) 260 N(759, 1002) 759. [718.3203, 799.4397]80
## 9 GOOG NAIVE(Close) 261 N(759, 1127) 759. [715.8599, 801.9001]80
## 10 GOOG NAIVE(Close) 262 N(759, 1252) 759. [713.5329, 804.2272]80
## # … with 1 more variable: `95%` <hilo>google_2015 %>%
model(NAIVE(Close)) %>%
forecast(h = 10) %>%
autoplot(google_2015) +
labs(title="Google daily closing stock price", y="$US" )
fit <- google_2015 %>%
model(NAIVE(Close))
sim <- fit %>% generate(h = 30, times = 5, bootstrap = TRUE)
sim## # A tsibble: 150 x 5 [1]
## # Key: Symbol, .model, .rep [5]
## Symbol .model day .rep .sim
## <chr> <chr> <dbl> <chr> <dbl>
## 1 GOOG NAIVE(Close) 253 1 759.
## 2 GOOG NAIVE(Close) 254 1 753.
## 3 GOOG NAIVE(Close) 255 1 749.
## 4 GOOG NAIVE(Close) 256 1 749.
## 5 GOOG NAIVE(Close) 257 1 748.
## 6 GOOG NAIVE(Close) 258 1 753.
## 7 GOOG NAIVE(Close) 259 1 748.
## 8 GOOG NAIVE(Close) 260 1 761.
## 9 GOOG NAIVE(Close) 261 1 751.
## 10 GOOG NAIVE(Close) 262 1 750.
## # … with 140 more rowsgoogle_2015 %>%
ggplot(aes(x = day)) +
geom_line(aes(y = Close)) +
geom_line(aes(y = .sim, colour = as.factor(.rep)),
data = sim) +
labs(title="Google daily closing stock price", y="$US" ) +
guides(colour = "none")
fc <- fit %>% forecast(h = 30, bootstrap = TRUE)
fc## # A fable: 30 x 5 [1]
## # Key: Symbol, .model [1]
## Symbol .model day Close .mean
## <chr> <chr> <dbl> <dist> <dbl>
## 1 GOOG NAIVE(Close) 253 sample[5000] 759.
## 2 GOOG NAIVE(Close) 254 sample[5000] 759.
## 3 GOOG NAIVE(Close) 255 sample[5000] 759.
## 4 GOOG NAIVE(Close) 256 sample[5000] 759.
## 5 GOOG NAIVE(Close) 257 sample[5000] 759.
## 6 GOOG NAIVE(Close) 258 sample[5000] 759.
## 7 GOOG NAIVE(Close) 259 sample[5000] 759.
## 8 GOOG NAIVE(Close) 260 sample[5000] 759.
## 9 GOOG NAIVE(Close) 261 sample[5000] 759.
## 10 GOOG NAIVE(Close) 262 sample[5000] 759.
## # … with 20 more rowsautoplot(fc, google_2015) +
labs(title="Google daily closing stock price", y="$US" )
google_2015 %>%
model(NAIVE(Close)) %>%
forecast(h = 10, bootstrap = TRUE, times = 1000) %>%
hilo()## # A tsibble: 10 x 7 [1]
## # Key: Symbol, .model [1]
## Symbol .model day Close .mean `80%`
## <chr> <chr> <dbl> <dist> <dbl> <hilo>
## 1 GOOG NAIVE(Close) 253 sample[1000] 759. [747.9560, 769.4825]80
## 2 GOOG NAIVE(Close) 254 sample[1000] 759. [742.1061, 774.5338]80
## 3 GOOG NAIVE(Close) 255 sample[1000] 759. [738.9679, 777.7889]80
## 4 GOOG NAIVE(Close) 256 sample[1000] 759. [735.2750, 781.2471]80
## 5 GOOG NAIVE(Close) 257 sample[1000] 758. [731.1173, 784.0799]80
## 6 GOOG NAIVE(Close) 258 sample[1000] 758. [727.7744, 788.5379]80
## 7 GOOG NAIVE(Close) 259 sample[1000] 758. [726.2142, 793.7744]80
## 8 GOOG NAIVE(Close) 260 sample[1000] 758. [721.7074, 796.6183]80
## 9 GOOG NAIVE(Close) 261 sample[1000] 758. [719.4406, 799.8316]80
## 10 GOOG NAIVE(Close) 262 sample[1000] 758. [717.9221, 802.4509]80
## # … with 1 more variable: `95%` <hilo>5.6 Forecasting using transformations 5.6 Forecasting using transformations
prices %>%
filter(!is.na(eggs)) %>%
model(RW(log(eggs) ~ drift())) %>%
forecast(h = 50) %>%
autoplot(prices %>% filter(!is.na(eggs)),
level = 80, point_forecast = lst(mean, median)
) +
labs(title = "Annual egg prices",
y = "$US (in cents adjusted for inflation) ")## Warning: Ignoring unknown aesthetics: linetype
# 5.7 Forecasting with decomposition
5.7 Forecasting with decomposition
us_retail_employment <- us_employment %>%
filter(year(Month) >= 1990, Title == "Retail Trade")
dcmp <- us_retail_employment %>%
model(STL(Employed ~ trend(window = 7), robust = TRUE)) %>%
components() %>%
select(-.model)
dcmp %>%
model(NAIVE(season_adjust)) %>%
forecast() %>%
autoplot(dcmp) +
labs(y = "Number of people",
title = "US retail employment")
fit_dcmp <- us_retail_employment %>%
model(stlf = decomposition_model(
STL(Employed ~ trend(window = 7), robust = TRUE),
NAIVE(season_adjust)
))
fit_dcmp %>%
forecast() %>%
autoplot(us_retail_employment)+
labs(y = "Number of people",
title = "US retail employment")
fit_dcmp %>% gg_tsresiduals()## Warning: Removed 12 row(s) containing missing values (geom_path).## Warning: Removed 12 rows containing missing values (geom_point).## Warning: Removed 12 rows containing non-finite values (stat_bin).
# 5.8 Evaluating point forecast accuracy
5.8 Evaluating point forecast accuracy
aus_production %>% filter(year(Quarter) >= 1995)## # A tsibble: 62 x 7 [1Q]
## Quarter Beer Tobacco Bricks Cement Electricity Gas
## <qtr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1995 Q1 426 4714 430 1626 41768 131
## 2 1995 Q2 408 3939 457 1703 43686 167
## 3 1995 Q3 416 6137 417 1733 46022 181
## 4 1995 Q4 520 4739 370 1545 42800 145
## 5 1996 Q1 409 4275 310 1526 43661 133
## 6 1996 Q2 398 5239 358 1593 44707 162
## 7 1996 Q3 398 6293 379 1706 46326 184
## 8 1996 Q4 507 5575 369 1699 43346 146
## 9 1997 Q1 432 4802 330 1511 43938 135
## 10 1997 Q2 398 5523 390 1785 45828 171
## # … with 52 more rowsaus_production %>% filter_index("1995 Q1" ~ .)## # A tsibble: 62 x 7 [1Q]
## Quarter Beer Tobacco Bricks Cement Electricity Gas
## <qtr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1995 Q1 426 4714 430 1626 41768 131
## 2 1995 Q2 408 3939 457 1703 43686 167
## 3 1995 Q3 416 6137 417 1733 46022 181
## 4 1995 Q4 520 4739 370 1545 42800 145
## 5 1996 Q1 409 4275 310 1526 43661 133
## 6 1996 Q2 398 5239 358 1593 44707 162
## 7 1996 Q3 398 6293 379 1706 46326 184
## 8 1996 Q4 507 5575 369 1699 43346 146
## 9 1997 Q1 432 4802 330 1511 43938 135
## 10 1997 Q2 398 5523 390 1785 45828 171
## # … with 52 more rowsaus_production %>%
slice(n()-19:0)## # A tsibble: 20 x 7 [1Q]
## Quarter Beer Tobacco Bricks Cement Electricity Gas
## <qtr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2005 Q3 408 NA NA 2340 56043 221
## 2 2005 Q4 482 NA NA 2265 54992 180
## 3 2006 Q1 438 NA NA 2027 57112 171
## 4 2006 Q2 386 NA NA 2278 57157 224
## 5 2006 Q3 405 NA NA 2427 58400 233
## 6 2006 Q4 491 NA NA 2451 56249 192
## 7 2007 Q1 427 NA NA 2140 56244 187
## 8 2007 Q2 383 NA NA 2362 55036 234
## 9 2007 Q3 394 NA NA 2536 59806 245
## 10 2007 Q4 473 NA NA 2562 56411 205
## 11 2008 Q1 420 NA NA 2183 59118 194
## 12 2008 Q2 390 NA NA 2558 56660 229
## 13 2008 Q3 410 NA NA 2612 64067 249
## 14 2008 Q4 488 NA NA 2373 59045 203
## 15 2009 Q1 415 NA NA 1963 58368 196
## 16 2009 Q2 398 NA NA 2160 57471 238
## 17 2009 Q3 419 NA NA 2325 58394 252
## 18 2009 Q4 488 NA NA 2273 57336 210
## 19 2010 Q1 414 NA NA 1904 58309 205
## 20 2010 Q2 374 NA NA 2401 58041 236aus_retail %>%
group_by(State, Industry) %>%
slice(1:12)## # A tsibble: 1,824 x 5 [1M]
## # Key: State, Industry [152]
## # Groups: State, Industry [152]
## State Industry `Series ID` Month Turnover
## <chr> <chr> <chr> <mth> <dbl>
## 1 Australian Capital Territory Cafes, restaurant… A3349849A 1982 Apr 4.4
## 2 Australian Capital Territory Cafes, restaurant… A3349849A 1982 May 3.4
## 3 Australian Capital Territory Cafes, restaurant… A3349849A 1982 Jun 3.6
## 4 Australian Capital Territory Cafes, restaurant… A3349849A 1982 Jul 4
## 5 Australian Capital Territory Cafes, restaurant… A3349849A 1982 Aug 3.6
## 6 Australian Capital Territory Cafes, restaurant… A3349849A 1982 Sep 4.2
## 7 Australian Capital Territory Cafes, restaurant… A3349849A 1982 Oct 4.8
## 8 Australian Capital Territory Cafes, restaurant… A3349849A 1982 Nov 5.4
## 9 Australian Capital Territory Cafes, restaurant… A3349849A 1982 Dec 6.9
## 10 Australian Capital Territory Cafes, restaurant… A3349849A 1983 Jan 3.8
## # … with 1,814 more rowsrecent_production <- aus_production %>%
filter(year(Quarter) >= 1992)
beer_train <- recent_production %>%
filter(year(Quarter) <= 2007)
beer_fit <- beer_train %>%
model(
Mean = MEAN(Beer),
`Naïve` = NAIVE(Beer),
`Seasonal naïve` = SNAIVE(Beer),
Drift = RW(Beer ~ drift())
)
beer_fc <- beer_fit %>%
forecast(h = 10)
beer_fc %>%
autoplot(
aus_production %>% filter(year(Quarter) >= 1992),
level = NULL
) +
labs(
y = "Megalitres",
title = "Forecasts for quarterly beer production"
) +
guides(colour = guide_legend(title = "Forecast"))
accuracy(beer_fc, recent_production)## # A tibble: 4 × 10
## .model .type ME RMSE MAE MPE MAPE MASE RMSSE ACF1
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Drift Test -54.0 64.9 58.9 -13.6 14.6 4.12 3.87 -0.0741
## 2 Mean Test -13.8 38.4 34.8 -3.97 8.28 2.44 2.29 -0.0691
## 3 Naïve Test -51.4 62.7 57.4 -13.0 14.2 4.01 3.74 -0.0691
## 4 Seasonal naïve Test 5.2 14.3 13.4 1.15 3.17 0.937 0.853 0.132google_fit <- google_2015 %>%
model(
Mean = MEAN(Close),
`Naïve` = NAIVE(Close),
Drift = RW(Close ~ drift())
)
google_fc <- google_fit %>%
forecast(google_jan_2016)
google_fc %>%
autoplot(bind_rows(google_2015, google_jan_2016),
level = NULL) +
labs(y = "$US",
title = "Google closing stock prices from Jan 2015") +
guides(colour = guide_legend(title = "Forecast"))
accuracy(google_fc, google_stock)## # A tibble: 3 × 11
## .model Symbol .type ME RMSE MAE MPE MAPE MASE RMSSE ACF1
## <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Drift GOOG Test -49.8 53.1 49.8 -6.99 6.99 6.99 4.74 0.604
## 2 Mean GOOG Test 117. 118. 117. 16.2 16.2 16.4 10.5 0.496
## 3 Naïve GOOG Test -40.4 43.4 40.4 -5.67 5.67 5.67 3.88 0.4965.9 Evaluating distributional forecast accuracy 5.9 Evaluating distributional forecast accuracy
google_fc %>%
filter(.model == "Naïve") %>%
autoplot(bind_rows(google_2015, google_jan_2016), level=80)+
labs(y = "$US",
title = "Google closing stock prices")
google_fc %>%
filter(.model == "Naïve", Date == "2016-01-04") %>%
accuracy(google_stock, list(qs=quantile_score), probs=0.10)## # A tibble: 1 × 4
## .model Symbol .type qs
## <chr> <chr> <chr> <dbl>
## 1 Naïve GOOG Test 4.86google_fc %>%
filter(.model == "Naïve", Date == "2016-01-04") %>%
accuracy(google_stock,
list(winkler = winkler_score), level = 80)## # A tibble: 1 × 4
## .model Symbol .type winkler
## <chr> <chr> <chr> <dbl>
## 1 Naïve GOOG Test 55.7google_fc %>%
accuracy(google_stock, list(crps = CRPS))## # A tibble: 3 × 4
## .model Symbol .type crps
## <chr> <chr> <chr> <dbl>
## 1 Drift GOOG Test 33.5
## 2 Mean GOOG Test 76.7
## 3 Naïve GOOG Test 26.5google_fc %>%
accuracy(google_stock, list(skill = skill_score(CRPS)))## # A tibble: 3 × 4
## .model Symbol .type skill
## <chr> <chr> <chr> <dbl>
## 1 Drift GOOG Test -0.266
## 2 Mean GOOG Test -1.90
## 3 Naïve GOOG Test 05.10 Time series cross-validation 5.10 Time series cross-validation
# Time series cross-validation accuracy
google_2015_tr <- google_2015 %>%
stretch_tsibble(.init = 3, .step = 1) %>%
relocate(Date, Symbol, .id)
google_2015_tr## # A tsibble: 31,875 x 10 [1]
## # Key: Symbol, .id [250]
## Date Symbol .id Open High Low Close Adj_Close Volume day
## <date> <chr> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
## 1 2015-01-02 GOOG 1 526. 528. 521. 522. 522. 1447600 1
## 2 2015-01-05 GOOG 1 520. 521. 510. 511. 511. 2059800 2
## 3 2015-01-06 GOOG 1 512. 513. 498. 499. 499. 2899900 3
## 4 2015-01-02 GOOG 2 526. 528. 521. 522. 522. 1447600 1
## 5 2015-01-05 GOOG 2 520. 521. 510. 511. 511. 2059800 2
## 6 2015-01-06 GOOG 2 512. 513. 498. 499. 499. 2899900 3
## 7 2015-01-07 GOOG 2 504. 504. 497. 498. 498. 2065100 4
## 8 2015-01-02 GOOG 3 526. 528. 521. 522. 522. 1447600 1
## 9 2015-01-05 GOOG 3 520. 521. 510. 511. 511. 2059800 2
## 10 2015-01-06 GOOG 3 512. 513. 498. 499. 499. 2899900 3
## # … with 31,865 more rows# TSCV accuracy
google_2015_tr %>%
model(RW(Close ~ drift())) %>%
forecast(h = 1) %>%
accuracy(google_2015)## Warning: The future dataset is incomplete, incomplete out-of-sample data will be treated as missing.
## 1 observation is missing at 253## # A tibble: 1 × 11
## .model Symbol .type ME RMSE MAE MPE MAPE MASE RMSSE ACF1
## <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 RW(Close ~ drif… GOOG Test 0.726 11.3 7.26 0.112 1.19 1.02 1.01 0.0985# Training set accuracy
google_2015 %>%
model(RW(Close ~ drift())) %>%
accuracy()## # A tibble: 1 × 11
## Symbol .model .type ME RMSE MAE MPE MAPE MASE RMSSE ACF1
## <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 GOOG RW(Close … Trai… -3.04e-14 11.1 7.16 -0.0267 1.18 1.00 0.996 0.0976google_2015_tr <- google_2015 %>%
stretch_tsibble(.init = 3, .step = 1)
fc <- google_2015_tr %>%
model(RW(Close ~ drift())) %>%
forecast(h = 8) %>%
group_by(.id) %>%
mutate(h = row_number()) %>%
ungroup() %>%
as_fable(response = "Close", distribution = Close)
fc %>%
accuracy(google_2015, by = c("h", ".model")) %>%
ggplot(aes(x = h, y = RMSE)) +
geom_point()## Warning: The future dataset is incomplete, incomplete out-of-sample data will be treated as missing.
## 8 observations are missing between 253 and 260
# 5.11 Exercises
5.11 Exercises
# Extract data of interest
recent_production <- aus_production %>%
filter(year(Quarter) >= 1992)
# Define and estimate a model
fit <- recent_production %>% model(SNAIVE(Beer))
# Look at the residuals
fit %>% gg_tsresiduals()## Warning: Removed 4 row(s) containing missing values (geom_path).## Warning: Removed 4 rows containing missing values (geom_point).## Warning: Removed 4 rows containing non-finite values (stat_bin).
# Look a some forecasts
fit %>% forecast() %>% autoplot(recent_production)
myseries_train <- myseries %>%
filter(year(Month) < 2011)
autoplot(myseries, Turnover) +
autolayer(myseries_train, Turnover, colour = "red")
fit <- myseries_train %>%
model(SNAIVE(Turnover))
fit %>% gg_tsresiduals()## Warning: Removed 12 row(s) containing missing values (geom_path).## Warning: Removed 12 rows containing missing values (geom_point).## Warning: Removed 12 rows containing non-finite values (stat_bin).
fc <- fit %>%
forecast(new_data = anti_join(myseries, myseries_train))## Joining, by = c("State", "Industry", "Series ID", "Month", "Turnover")fc %>% autoplot(myseries)
fit %>% accuracy()## # A tibble: 1 × 12
## State Industry .model .type ME RMSE MAE MPE MAPE MASE RMSSE ACF1
## <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Norther… Clothin… SNAIV… Trai… 0.439 1.21 0.915 5.23 12.4 1 1 0.768fc %>% accuracy(myseries)## # A tibble: 1 × 12
## .model State Industry .type ME RMSE MAE MPE MAPE MASE RMSSE ACF1
## <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 SNAIVE(T… Nort… Clothin… Test 0.836 1.55 1.24 5.94 9.06 1.36 1.28 0.6017.1 The linear model 7.1 The linear model
us_change %>%
pivot_longer(c(Consumption, Income), names_to="Series") %>%
autoplot(value) +
labs(y = "% change")
us_change %>%
ggplot(aes(x = Income, y = Consumption)) +
labs(y = "Consumption (quarterly % change)",
x = "Income (quarterly % change)") +
geom_point() +
geom_smooth(method = "lm", se = FALSE)## `geom_smooth()` using formula 'y ~ x'
us_change %>%
model(TSLM(Consumption ~ Income)) %>%
report()## Series: Consumption
## Model: TSLM
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.58236 -0.27777 0.01862 0.32330 1.42229
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.54454 0.05403 10.079 < 2e-16 ***
## Income 0.27183 0.04673 5.817 2.4e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5905 on 196 degrees of freedom
## Multiple R-squared: 0.1472, Adjusted R-squared: 0.1429
## F-statistic: 33.84 on 1 and 196 DF, p-value: 2.4022e-08us_change %>%
select(-Consumption, -Income) %>%
pivot_longer(-Quarter) %>%
ggplot(aes(Quarter, value, colour = name)) +
geom_line() +
facet_grid(name ~ ., scales = "free_y") +
guides(colour = "none") +
labs(y="% change")
us_change %>%
GGally::ggpairs(columns = 2:6)## Registered S3 method overwritten by 'GGally':
## method from
## +.gg ggplot2
# 7.2 Least squares estimation
7.2 Least squares estimation
fit_consMR <- us_change %>%
model(tslm = TSLM(Consumption ~ Income + Production +
Unemployment + Savings))
report(fit_consMR)## Series: Consumption
## Model: TSLM
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.90555 -0.15821 -0.03608 0.13618 1.15471
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.253105 0.034470 7.343 5.71e-12 ***
## Income 0.740583 0.040115 18.461 < 2e-16 ***
## Production 0.047173 0.023142 2.038 0.0429 *
## Unemployment -0.174685 0.095511 -1.829 0.0689 .
## Savings -0.052890 0.002924 -18.088 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3102 on 193 degrees of freedom
## Multiple R-squared: 0.7683, Adjusted R-squared: 0.7635
## F-statistic: 160 on 4 and 193 DF, p-value: < 2.22e-16augment(fit_consMR) %>%
ggplot(aes(x = Quarter)) +
geom_line(aes(y = Consumption, colour = "Data")) +
geom_line(aes(y = .fitted, colour = "Fitted")) +
labs(y = NULL,
title = "Percent change in US consumption expenditure"
) +
scale_colour_manual(values=c(Data="black",Fitted="#D55E00")) +
guides(colour = guide_legend(title = NULL))
augment(fit_consMR) %>%
ggplot(aes(x = Consumption, y = .fitted)) +
geom_point() +
labs(
y = "Fitted (predicted values)",
x = "Data (actual values)",
title = "Percent change in US consumption expenditure"
) +
geom_abline(intercept = 0, slope = 1)
# 7.3 Evaluating the regression model
7.3 Evaluating the regression model
fit_consMR %>% gg_tsresiduals()
augment(fit_consMR) %>%
features(.innov, ljung_box, lag = 10, dof = 5)## # A tibble: 1 × 3
## .model lb_stat lb_pvalue
## <chr> <dbl> <dbl>
## 1 tslm 18.9 0.00204us_change %>%
left_join(residuals(fit_consMR), by = "Quarter") %>%
pivot_longer(Income:Unemployment,
names_to = "regressor", values_to = "x") %>%
ggplot(aes(x = x, y = .resid)) +
geom_point() +
facet_wrap(. ~ regressor, scales = "free_x") +
labs(y = "Residuals", x = "")
augment(fit_consMR) %>%
ggplot(aes(x = .fitted, y = .resid)) +
geom_point() + labs(x = "Fitted", y = "Residuals")
fit <- aus_airpassengers %>%
filter(Year <= 2011) %>%
left_join(guinea_rice, by = "Year") %>%
model(TSLM(Passengers ~ Production))
report(fit)## Series: Passengers
## Model: TSLM
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9448 -1.8917 -0.3272 1.8620 10.4210
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -7.493 1.203 -6.229 2.25e-07 ***
## Production 40.288 1.337 30.135 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.239 on 40 degrees of freedom
## Multiple R-squared: 0.9578, Adjusted R-squared: 0.9568
## F-statistic: 908.1 on 1 and 40 DF, p-value: < 2.22e-16fit %>% gg_tsresiduals()
# 7.4 Some useful predictors
7.4 Some useful predictors
recent_production <- aus_production %>%
filter(year(Quarter) >= 1992)
recent_production %>%
autoplot(Beer) +
labs(y = "Megalitres",
title = "Australian quarterly beer production")
fit_beer <- recent_production %>%
model(TSLM(Beer ~ trend() + season()))
report(fit_beer)## Series: Beer
## Model: TSLM
##
## Residuals:
## Min 1Q Median 3Q Max
## -42.9029 -7.5995 -0.4594 7.9908 21.7895
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 441.80044 3.73353 118.333 < 2e-16 ***
## trend() -0.34027 0.06657 -5.111 2.73e-06 ***
## season()year2 -34.65973 3.96832 -8.734 9.10e-13 ***
## season()year3 -17.82164 4.02249 -4.430 3.45e-05 ***
## season()year4 72.79641 4.02305 18.095 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 12.23 on 69 degrees of freedom
## Multiple R-squared: 0.9243, Adjusted R-squared: 0.9199
## F-statistic: 210.7 on 4 and 69 DF, p-value: < 2.22e-16augment(fit_beer) %>%
ggplot(aes(x = Quarter)) +
geom_line(aes(y = Beer, colour = "Data")) +
geom_line(aes(y = .fitted, colour = "Fitted")) +
scale_colour_manual(
values = c(Data = "black", Fitted = "#D55E00")
) +
labs(y = "Megalitres",
title = "Australian quarterly beer production") +
guides(colour = guide_legend(title = "Series"))
augment(fit_beer) %>%
ggplot(aes(x = Beer, y = .fitted,
colour = factor(quarter(Quarter)))) +
geom_point() +
labs(y = "Fitted", x = "Actual values",
title = "Australian quarterly beer production") +
geom_abline(intercept = 0, slope = 1) +
guides(colour = guide_legend(title = "Quarter"))
fourier_beer <- recent_production %>%
model(TSLM(Beer ~ trend() + fourier(K = 2)))
report(fourier_beer)## Series: Beer
## Model: TSLM
##
## Residuals:
## Min 1Q Median 3Q Max
## -42.9029 -7.5995 -0.4594 7.9908 21.7895
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 446.87920 2.87321 155.533 < 2e-16 ***
## trend() -0.34027 0.06657 -5.111 2.73e-06 ***
## fourier(K = 2)C1_4 8.91082 2.01125 4.430 3.45e-05 ***
## fourier(K = 2)S1_4 -53.72807 2.01125 -26.714 < 2e-16 ***
## fourier(K = 2)C2_4 -13.98958 1.42256 -9.834 9.26e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 12.23 on 69 degrees of freedom
## Multiple R-squared: 0.9243, Adjusted R-squared: 0.9199
## F-statistic: 210.7 on 4 and 69 DF, p-value: < 2.22e-167.5 Selecting predictors 7.5 Selecting predictors
glance(fit_consMR) %>%
select(adj_r_squared, CV, AIC, AICc, BIC)## # A tibble: 1 × 5
## adj_r_squared CV AIC AICc BIC
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0.763 0.104 -457. -456. -437.7.6 Forecasting with regression 7.6 Forecasting with regression
recent_production <- aus_production %>%
filter(year(Quarter) >= 1992)
fit_beer <- recent_production %>%
model(TSLM(Beer ~ trend() + season()))
fc_beer <- forecast(fit_beer)
fc_beer %>%
autoplot(recent_production) +
labs(
title = "Forecasts of beer production using regression",
y = "megalitres"
)
fit_consBest <- us_change %>%
model(
lm = TSLM(Consumption ~ Income + Savings + Unemployment)
)
future_scenarios <- scenarios(
Increase = new_data(us_change, 4) %>%
mutate(Income=1, Savings=0.5, Unemployment=0),
Decrease = new_data(us_change, 4) %>%
mutate(Income=-1, Savings=-0.5, Unemployment=0),
names_to = "Scenario")
fc <- forecast(fit_consBest, new_data = future_scenarios)
us_change %>%
autoplot(Consumption) +
autolayer(fc) +
labs(title = "US consumption", y = "% change")
fit_cons <- us_change %>%
model(TSLM(Consumption ~ Income))
new_cons <- scenarios(
"Average increase" = new_data(us_change, 4) %>%
mutate(Income = mean(us_change$Income)),
"Extreme increase" = new_data(us_change, 4) %>%
mutate(Income = 12),
names_to = "Scenario"
)
fcast <- forecast(fit_cons, new_cons)
us_change %>%
autoplot(Consumption) +
autolayer(fcast) +
labs(title = "US consumption", y = "% change")
# 7.7 Nonlinear regression
7.7 Nonlinear regression
boston_men <- boston_marathon %>%
filter(Year >= 1924) %>%
filter(Event == "Men's open division") %>%
mutate(Minutes = as.numeric(Time)/60)
fit_trends <- boston_men %>%
model(
linear = TSLM(Minutes ~ trend()),
exponential = TSLM(log(Minutes) ~ trend()),
piecewise = TSLM(Minutes ~ trend(knots = c(1950, 1980)))
)
fc_trends <- fit_trends %>% forecast(h = 10)
boston_men %>%
autoplot(Minutes) +
geom_line(data = fitted(fit_trends),
aes(y = .fitted, colour = .model)) +
autolayer(fc_trends, alpha = 0.5, level = 95) +
labs(y = "Minutes",
title = "Boston marathon winning times")
7.10 Exercises 7.10 Exercises
jan14_vic_elec <- vic_elec %>%
filter(yearmonth(Time) == yearmonth("2014 Jan")) %>%
index_by(Date = as_date(Time)) %>%
summarise(
Demand = sum(Demand),
Temperature = max(Temperature)
)
jan14_vic_elec %>%
model(TSLM(Demand ~ Temperature)) %>%
forecast(
new_data(jan14_vic_elec, 1) %>%
mutate(Temperature = 15)
) %>%
autoplot(jan14_vic_elec)
8.1 Simple exponential smoothing 8.1 Simple exponential smoothing
algeria_economy <- global_economy %>%
filter(Country == "Algeria")
algeria_economy %>%
autoplot(Exports) +
labs(y = "% of GDP", title = "Exports: Algeria")
# Estimate parameters
fit <- algeria_economy %>%
model(ETS(Exports ~ error("A") + trend("N") + season("N")))
fc <- fit %>%
forecast(h = 5)
fc %>%
autoplot(algeria_economy) +
geom_line(aes(y = .fitted), col="#D55E00",
data = augment(fit)) +
labs(y="% of GDP", title="Exports: Algeria") +
guides(colour = "none")
# 8.2 Methods with trend
8.2 Methods with trend
aus_economy <- global_economy %>%
filter(Code == "AUS") %>%
mutate(Pop = Population / 1e6)
autoplot(aus_economy, Pop) +
labs(y = "Millions", title = "Australian population")
fit <- aus_economy %>%
model(
AAN = ETS(Pop ~ error("A") + trend("A") + season("N"))
)
fc <- fit %>% forecast(h = 10)
aus_economy %>%
model(
`Holt's method` = ETS(Pop ~ error("A") +
trend("A") + season("N")),
`Damped Holt's method` = ETS(Pop ~ error("A") +
trend("Ad", phi = 0.9) + season("N"))
) %>%
forecast(h = 15) %>%
autoplot(aus_economy, level = NULL) +
labs(title = "Australian population",
y = "Millions") +
guides(colour = guide_legend(title = "Forecast"))
www_usage <- as_tsibble(WWWusage)
www_usage %>% autoplot(value) +
labs(x="Minute", y="Number of users",
title = "Internet usage per minute")
www_usage %>%
stretch_tsibble(.init = 10) %>%
model(
SES = ETS(value ~ error("A") + trend("N") + season("N")),
Holt = ETS(value ~ error("A") + trend("A") + season("N")),
Damped = ETS(value ~ error("A") + trend("Ad") +
season("N"))
) %>%
forecast(h = 1) %>%
accuracy(www_usage)## Warning: The future dataset is incomplete, incomplete out-of-sample data will be treated as missing.
## 1 observation is missing at 101## # A tibble: 3 × 10
## .model .type ME RMSE MAE MPE MAPE MASE RMSSE ACF1
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Damped Test 0.288 3.69 3.00 0.347 2.26 0.663 0.636 0.336
## 2 Holt Test 0.0610 3.87 3.17 0.244 2.38 0.701 0.668 0.296
## 3 SES Test 1.46 6.05 4.81 0.904 3.55 1.06 1.04 0.803fit <- www_usage %>%
model(
Damped = ETS(value ~ error("A") + trend("Ad") +
season("N"))
)
# Estimated parameters:
tidy(fit)## # A tibble: 5 × 3
## .model term estimate
## <chr> <chr> <dbl>
## 1 Damped alpha 1.00
## 2 Damped beta 0.997
## 3 Damped phi 0.815
## 4 Damped l[0] 90.4
## 5 Damped b[0] -0.0173fit %>%
forecast(h = 10) %>%
autoplot(www_usage) +
labs(x="Minute", y="Number of users",
title = "Internet usage per minute")
# 8.3 Methods with seasonality
8.3 Methods with seasonality
aus_holidays <- tourism %>%
filter(Purpose == "Holiday") %>%
summarise(Trips = sum(Trips)/1e3)
fit <- aus_holidays %>%
model(
additive = ETS(Trips ~ error("A") + trend("A") +
season("A")),
multiplicative = ETS(Trips ~ error("M") + trend("A") +
season("M"))
)
fc <- fit %>% forecast(h = "3 years")
fc %>%
autoplot(aus_holidays, level = NULL) +
labs(title="Australian domestic tourism",
y="Overnight trips (millions)") +
guides(colour = guide_legend(title = "Forecast"))
sth_cross_ped <- pedestrian %>%
filter(Date >= "2016-07-01",
Sensor == "Southern Cross Station") %>%
index_by(Date) %>%
summarise(Count = sum(Count)/1000)
sth_cross_ped %>%
filter(Date <= "2016-07-31") %>%
model(
hw = ETS(Count ~ error("M") + trend("Ad") + season("M"))
) %>%
forecast(h = "2 weeks") %>%
autoplot(sth_cross_ped %>% filter(Date <= "2016-08-14")) +
labs(title = "Daily traffic: Southern Cross",
y="Pedestrians ('000)")
8.6 Estimation and model selection 8.6 Estimation and model selection
aus_holidays <- tourism %>%
filter(Purpose == "Holiday") %>%
summarise(Trips = sum(Trips)/1e3)
fit <- aus_holidays %>%
model(ETS(Trips))
report(fit)## Series: Trips
## Model: ETS(M,N,A)
## Smoothing parameters:
## alpha = 0.3484054
## gamma = 0.0001000018
##
## Initial states:
## l[0] s[0] s[-1] s[-2] s[-3]
## 9.727072 -0.5376106 -0.6884343 -0.2933663 1.519411
##
## sigma^2: 0.0022
##
## AIC AICc BIC
## 226.2289 227.7845 242.9031components(fit) %>%
autoplot() +
labs(title = "ETS(M,N,A) components")## Warning: Removed 4 row(s) containing missing values (geom_path).
# 8.7 Forecasting with ETS models
8.7 Forecasting with ETS models
fit %>%
forecast(h = 8) %>%
autoplot(aus_holidays)+
labs(title="Australian domestic tourism",
y="Overnight trips (millions)")
9.1 Stationarity and differencing 9.1 Stationarity and differencing
google_2015 <- gafa_stock %>%
filter(Symbol == "GOOG", year(Date) == 2015)
google_2015 %>% ACF(Close) %>%
autoplot() + labs(subtitle = "Google closing stock price")## Warning: Provided data has an irregular interval, results should be treated with
## caution. Computing ACF by observation.
google_2015 %>% ACF(difference(Close)) %>%
autoplot() + labs(subtitle = "Changes in Google closing stock price")## Warning: Provided data has an irregular interval, results should be treated with
## caution. Computing ACF by observation.
google_2015 %>%
mutate(diff_close = difference(Close)) %>%
features(diff_close, ljung_box, lag = 10)## # A tibble: 1 × 3
## Symbol lb_stat lb_pvalue
## <chr> <dbl> <dbl>
## 1 GOOG 7.91 0.637PBS %>%
filter(ATC2 == "H02") %>%
summarise(Cost = sum(Cost)/1e6) %>%
transmute(
`Sales ($million)` = Cost,
`Log sales` = log(Cost),
`Annual change in log sales` = difference(log(Cost), 12),
`Doubly differenced log sales` =
difference(difference(log(Cost), 12), 1)
) %>%
pivot_longer(-Month, names_to="Type", values_to="Sales") %>%
mutate(
Type = factor(Type, levels = c(
"Sales ($million)",
"Log sales",
"Annual change in log sales",
"Doubly differenced log sales"))
) %>%
ggplot(aes(x = Month, y = Sales)) +
geom_line() +
facet_grid(vars(Type), scales = "free_y") +
labs(title = "Corticosteroid drug sales", y = NULL)
google_2015 %>%
features(Close, unitroot_kpss)## # A tibble: 1 × 3
## Symbol kpss_stat kpss_pvalue
## <chr> <dbl> <dbl>
## 1 GOOG 3.56 0.01google_2015 %>%
mutate(diff_close = difference(Close)) %>%
features(diff_close, unitroot_kpss)## # A tibble: 1 × 3
## Symbol kpss_stat kpss_pvalue
## <chr> <dbl> <dbl>
## 1 GOOG 0.0989 0.1google_2015 %>%
features(Close, unitroot_ndiffs)## # A tibble: 1 × 2
## Symbol ndiffs
## <chr> <int>
## 1 GOOG 1aus_total_retail <- aus_retail %>%
summarise(Turnover = sum(Turnover))
aus_total_retail %>%
mutate(log_turnover = log(Turnover)) %>%
features(log_turnover, unitroot_nsdiffs)## # A tibble: 1 × 1
## nsdiffs
## <int>
## 1 1aus_total_retail %>%
mutate(log_turnover = difference(log(Turnover), 12)) %>%
features(log_turnover, unitroot_ndiffs)## # A tibble: 1 × 1
## ndiffs
## <int>
## 1 19.5 Non-seasonal ARIMA models 9.5 Non-seasonal ARIMA models
global_economy %>%
filter(Code == "EGY") %>%
autoplot(Exports) +
labs(y = "% of GDP", title = "Egyptian exports")
fit <- global_economy %>%
filter(Code == "EGY") %>%
model(ARIMA(Exports))
report(fit)## Series: Exports
## Model: ARIMA(2,0,1) w/ mean
##
## Coefficients:
## ar1 ar2 ma1 constant
## 1.6764 -0.8034 -0.6896 2.5623
## s.e. 0.1111 0.0928 0.1492 0.1161
##
## sigma^2 estimated as 8.046: log likelihood=-141.57
## AIC=293.13 AICc=294.29 BIC=303.43fit %>% forecast(h=10) %>%
autoplot(global_economy) +
labs(y = "% of GDP", title = "Egyptian exports")
global_economy %>%
filter(Code == "EGY") %>%
ACF(Exports) %>%
autoplot()
global_economy %>%
filter(Code == "EGY") %>%
PACF(Exports) %>%
autoplot()
fit2 <- global_economy %>%
filter(Code == "EGY") %>%
model(ARIMA(Exports ~ pdq(4,0,0)))
report(fit2)## Series: Exports
## Model: ARIMA(4,0,0) w/ mean
##
## Coefficients:
## ar1 ar2 ar3 ar4 constant
## 0.9861 -0.1715 0.1807 -0.3283 6.6922
## s.e. 0.1247 0.1865 0.1865 0.1273 0.3562
##
## sigma^2 estimated as 7.885: log likelihood=-140.53
## AIC=293.05 AICc=294.7 BIC=305.419.7 ARIMA modelling in fable 9.7 ARIMA modelling in fable
global_economy %>%
filter(Code == "CAF") %>%
autoplot(Exports) +
labs(title="Central African Republic exports",
y="% of GDP")
global_economy %>%
filter(Code == "CAF") %>%
gg_tsdisplay(difference(Exports), plot_type='partial')## Warning: Removed 1 row(s) containing missing values (geom_path).## Warning: Removed 1 rows containing missing values (geom_point).
caf_fit <- global_economy %>%
filter(Code == "CAF") %>%
model(arima210 = ARIMA(Exports ~ pdq(2,1,0)),
arima013 = ARIMA(Exports ~ pdq(0,1,3)),
stepwise = ARIMA(Exports),
search = ARIMA(Exports, stepwise=FALSE))
caf_fit %>% pivot_longer(!Country, names_to = "Model name",
values_to = "Orders")## # A mable: 4 x 3
## # Key: Country, Model name [4]
## Country `Model name` Orders
## <fct> <chr> <model>
## 1 Central African Republic arima210 <ARIMA(2,1,0)>
## 2 Central African Republic arima013 <ARIMA(0,1,3)>
## 3 Central African Republic stepwise <ARIMA(2,1,2)>
## 4 Central African Republic search <ARIMA(3,1,0)>glance(caf_fit) %>% arrange(AICc) %>% select(.model:BIC)## # A tibble: 4 × 6
## .model sigma2 log_lik AIC AICc BIC
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 search 6.52 -133. 274. 275. 282.
## 2 arima210 6.71 -134. 275. 275. 281.
## 3 arima013 6.54 -133. 274. 275. 282.
## 4 stepwise 6.42 -132. 274. 275. 284.caf_fit %>%
select(search) %>%
gg_tsresiduals()
augment(caf_fit) %>%
filter(.model=='search') %>%
features(.innov, ljung_box, lag = 10, dof = 3)## # A tibble: 1 × 4
## Country .model lb_stat lb_pvalue
## <fct> <chr> <dbl> <dbl>
## 1 Central African Republic search 5.75 0.569caf_fit %>%
forecast(h=5) %>%
filter(.model=='search') %>%
autoplot(global_economy)
gg_arma(caf_fit %>% select(Country, search))
9.9 Seasonal ARIMA models 9.9 Seasonal ARIMA models
leisure <- us_employment %>%
filter(Title == "Leisure and Hospitality",
year(Month) > 2000) %>%
mutate(Employed = Employed/1000) %>%
select(Month, Employed)
autoplot(leisure, Employed) +
labs(title = "US employment: leisure and hospitality",
y="Number of people (millions)")
leisure %>%
gg_tsdisplay(difference(Employed, 12),
plot_type='partial', lag=36) +
labs(title="Seasonally differenced", y="")## Warning: Removed 12 row(s) containing missing values (geom_path).## Warning: Removed 12 rows containing missing values (geom_point).
leisure %>%
gg_tsdisplay(difference(Employed, 12) %>% difference(),
plot_type='partial', lag=36) +
labs(title = "Double differenced", y="")## Warning: Removed 13 row(s) containing missing values (geom_path).## Warning: Removed 13 rows containing missing values (geom_point).
fit <- leisure %>%
model(
arima012011 = ARIMA(Employed ~ pdq(0,1,2) + PDQ(0,1,1)),
arima210011 = ARIMA(Employed ~ pdq(2,1,0) + PDQ(0,1,1)),
auto = ARIMA(Employed, stepwise = FALSE, approx = FALSE)
)
fit %>% pivot_longer(everything(), names_to = "Model name",
values_to = "Orders")## # A mable: 3 x 2
## # Key: Model name [3]
## `Model name` Orders
## <chr> <model>
## 1 arima012011 <ARIMA(0,1,2)(0,1,1)[12]>
## 2 arima210011 <ARIMA(2,1,0)(0,1,1)[12]>
## 3 auto <ARIMA(2,1,0)(1,1,1)[12]>glance(fit) %>% arrange(AICc) %>% select(.model:BIC)## # A tibble: 3 × 6
## .model sigma2 log_lik AIC AICc BIC
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 auto 0.00142 395. -780. -780. -763.
## 2 arima210011 0.00145 392. -776. -776. -763.
## 3 arima012011 0.00146 391. -775. -775. -761.fit %>% select(auto) %>% gg_tsresiduals(lag=36)
augment(fit) %>%
filter(.model == "auto") %>%
features(.innov, ljung_box, lag=24, dof=4)## # A tibble: 1 × 3
## .model lb_stat lb_pvalue
## <chr> <dbl> <dbl>
## 1 auto 16.6 0.680forecast(fit, h=36) %>%
filter(.model=='auto') %>%
autoplot(leisure) +
labs(title = "US employment: leisure and hospitality",
y="Number of people (millions)")
h02 <- PBS %>%
filter(ATC2 == "H02") %>%
summarise(Cost = sum(Cost)/1e6)
h02 %>%
mutate(log(Cost)) %>%
pivot_longer(-Month) %>%
ggplot(aes(x = Month, y = value)) +
geom_line() +
facet_grid(name ~ ., scales = "free_y") +
labs(y="", title="Corticosteroid drug scripts (H02)")
h02 %>% gg_tsdisplay(difference(log(Cost), 12),
plot_type='partial', lag_max = 24)## Warning: Removed 12 row(s) containing missing values (geom_path).## Warning: Removed 12 rows containing missing values (geom_point).
fit <- h02 %>%
model(ARIMA(log(Cost) ~ 0 + pdq(3,0,1) + PDQ(0,1,2)))
fit %>% gg_tsresiduals(lag_max=36)
augment(fit) %>%
features(.innov, ljung_box, lag = 36, dof = 6)## # A tibble: 1 × 3
## .model lb_stat lb_pvalue
## <chr> <dbl> <dbl>
## 1 ARIMA(log(Cost) ~ 0 + pdq(3, 0, 1) + PDQ(0, 1, 2)) 50.7 0.0104h02 %>%
model(ARIMA(log(Cost) ~ 0 + pdq(3,0,1) + PDQ(0,1,2))) %>%
forecast() %>%
autoplot(h02) +
labs(y=" $AU (millions)",
title="Corticosteroid drug scripts (H02) sales")
# 9.10 ARIMA vs ETS
9.10 ARIMA vs ETS
aus_economy <- global_economy %>%
filter(Code == "AUS") %>%
mutate(Population = Population/1e6)
aus_economy %>%
slice(-n()) %>%
stretch_tsibble(.init = 10) %>%
model(
ETS(Population),
ARIMA(Population)
) %>%
forecast(h = 1) %>%
accuracy(aus_economy) %>%
select(.model, RMSE:MAPE)## # A tibble: 2 × 5
## .model RMSE MAE MPE MAPE
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 ARIMA(Population) 0.194 0.0789 0.277 0.509
## 2 ETS(Population) 0.0774 0.0543 0.112 0.327aus_economy %>%
model(ETS(Population)) %>%
forecast(h = "5 years") %>%
autoplot(aus_economy %>% filter(Year >= 2000)) +
labs(title = "Australian population",
y = "People (millions)")
cement <- aus_production %>%
select(Cement) %>%
filter_index("1988 Q1" ~ .)
train <- cement %>% filter_index(. ~ "2007 Q4")
fit_arima <- train %>% model(ARIMA(Cement))
report(fit_arima)## Series: Cement
## Model: ARIMA(1,0,1)(2,1,1)[4] w/ drift
##
## Coefficients:
## ar1 ma1 sar1 sar2 sma1 constant
## 0.8886 -0.2366 0.081 -0.2345 -0.8979 5.3884
## s.e. 0.0842 0.1334 0.157 0.1392 0.1780 1.4844
##
## sigma^2 estimated as 11456: log likelihood=-463.52
## AIC=941.03 AICc=942.68 BIC=957.35fit_arima %>% gg_tsresiduals(lag_max = 16)
augment(fit_arima) %>%
features(.innov, ljung_box, lag = 16, dof = 6)## # A tibble: 1 × 3
## .model lb_stat lb_pvalue
## <chr> <dbl> <dbl>
## 1 ARIMA(Cement) 6.37 0.783fit_ets <- train %>% model(ETS(Cement))
report(fit_ets)## Series: Cement
## Model: ETS(M,N,M)
## Smoothing parameters:
## alpha = 0.7533714
## gamma = 0.0001000093
##
## Initial states:
## l[0] s[0] s[-1] s[-2] s[-3]
## 1694.712 1.031179 1.045209 1.011424 0.9121874
##
## sigma^2: 0.0034
##
## AIC AICc BIC
## 1104.095 1105.650 1120.769fit_ets %>%
gg_tsresiduals(lag_max = 16)
augment(fit_ets) %>%
features(.innov, ljung_box, lag = 16, dof = 6)## # A tibble: 1 × 3
## .model lb_stat lb_pvalue
## <chr> <dbl> <dbl>
## 1 ETS(Cement) 10.0 0.438# Generate forecasts and compare accuracy over the test set
bind_rows(
fit_arima %>% accuracy(),
fit_ets %>% accuracy(),
fit_arima %>% forecast(h = 10) %>% accuracy(cement),
fit_ets %>% forecast(h = 10) %>% accuracy(cement)
) %>%
select(-ME, -MPE, -ACF1)## # A tibble: 4 × 7
## .model .type RMSE MAE MAPE MASE RMSSE
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 ARIMA(Cement) Training 100. 79.9 4.37 0.546 0.582
## 2 ETS(Cement) Training 103. 80.0 4.41 0.547 0.596
## 3 ARIMA(Cement) Test 216. 186. 8.68 1.27 1.26
## 4 ETS(Cement) Test 222. 191. 8.85 1.30 1.29cement %>%
model(ARIMA(Cement)) %>%
forecast(h="3 years") %>%
autoplot(cement) +
labs(title = "Cement production in Australia",
y = "Tonnes ('000)")
10.2 Regression with ARIMA errors using fable 10.2 Regression with ARIMA errors using fable
ARIMA(y ~ x + pdq(1,1,0))## <ARIMA model definition>us_change %>%
pivot_longer(c(Consumption, Income),
names_to = "var", values_to = "value") %>%
ggplot(aes(x = Quarter, y = value)) +
geom_line() +
facet_grid(vars(var), scales = "free_y") +
labs(title = "US consumption and personal income",
y = "Quarterly % change")
fit <- us_change %>%
model(ARIMA(Consumption ~ Income))
report(fit)## Series: Consumption
## Model: LM w/ ARIMA(1,0,2) errors
##
## Coefficients:
## ar1 ma1 ma2 Income intercept
## 0.7070 -0.6172 0.2066 0.1976 0.5949
## s.e. 0.1068 0.1218 0.0741 0.0462 0.0850
##
## sigma^2 estimated as 0.3113: log likelihood=-163.04
## AIC=338.07 AICc=338.51 BIC=357.8bind_rows(
`Regression residuals` =
as_tibble(residuals(fit, type = "regression")),
`ARIMA residuals` =
as_tibble(residuals(fit, type = "innovation")),
.id = "type"
) %>%
mutate(
type = factor(type, levels=c(
"Regression residuals", "ARIMA residuals"))
) %>%
ggplot(aes(x = Quarter, y = .resid)) +
geom_line() +
facet_grid(vars(type))
fit %>% gg_tsresiduals()
augment(fit) %>%
features(.innov, ljung_box, dof = 5, lag = 8)## # A tibble: 1 × 3
## .model lb_stat lb_pvalue
## <chr> <dbl> <dbl>
## 1 ARIMA(Consumption ~ Income) 5.21 0.15710.3 Forecasting 10.3 Forecasting
us_change_future <- new_data(us_change, 8) %>%
mutate(Income = mean(us_change$Income))
forecast(fit, new_data = us_change_future) %>%
autoplot(us_change) +
labs(y = "Percentage change")
vic_elec_daily <- vic_elec %>%
filter(year(Time) == 2014) %>%
index_by(Date = date(Time)) %>%
summarise(
Demand = sum(Demand) / 1e3,
Temperature = max(Temperature),
Holiday = any(Holiday)
) %>%
mutate(Day_Type = case_when(
Holiday ~ "Holiday",
wday(Date) %in% 2:6 ~ "Weekday",
TRUE ~ "Weekend"
))
vic_elec_daily %>%
ggplot(aes(x = Temperature, y = Demand, colour = Day_Type)) +
geom_point() +
labs(y = "Electricity demand (GW)",
x = "Maximum daily temperature")
vic_elec_daily %>%
pivot_longer(c(Demand, Temperature)) %>%
ggplot(aes(x = Date, y = value)) +
geom_line() +
facet_grid(name ~ ., scales = "free_y") + ylab("")
fit <- vic_elec_daily %>%
model(ARIMA(Demand ~ Temperature + I(Temperature^2) +
(Day_Type == "Weekday")))
fit %>% gg_tsresiduals()
augment(fit) %>%
features(.innov, ljung_box, dof = 9, lag = 14)## # A tibble: 1 × 3
## .model lb_stat lb_pvalue
## <chr> <dbl> <dbl>
## 1 "ARIMA(Demand ~ Temperature + I(Temperature^2) + (Day_Type … 28.4 0.0000304vic_elec_future <- new_data(vic_elec_daily, 14) %>%
mutate(
Temperature = 26,
Holiday = c(TRUE, rep(FALSE, 13)),
Day_Type = case_when(
Holiday ~ "Holiday",
wday(Date) %in% 2:6 ~ "Weekday",
TRUE ~ "Weekend"
)
)
forecast(fit, vic_elec_future) %>%
autoplot(vic_elec_daily) +
labs(title="Daily electricity demand: Victoria",
y="GW")
# 10.4 Stochastic and deterministic trends
10.4 Stochastic and deterministic trends
aus_airpassengers %>%
autoplot(Passengers) +
labs(y = "Passengers (millions)",
title = "Total annual air passengers")
fit_deterministic <- aus_airpassengers %>%
model(deterministic = ARIMA(Passengers ~ 1 + trend() +
pdq(d = 0)))
report(fit_deterministic)## Series: Passengers
## Model: LM w/ ARIMA(1,0,0) errors
##
## Coefficients:
## ar1 trend() intercept
## 0.9564 1.4151 0.9014
## s.e. 0.0362 0.1972 7.0751
##
## sigma^2 estimated as 4.343: log likelihood=-100.88
## AIC=209.77 AICc=210.72 BIC=217.17fit_stochastic <- aus_airpassengers %>%
model(stochastic = ARIMA(Passengers ~ pdq(d = 1)))
report(fit_stochastic)## Series: Passengers
## Model: ARIMA(0,1,0) w/ drift
##
## Coefficients:
## constant
## 1.4191
## s.e. 0.3014
##
## sigma^2 estimated as 4.271: log likelihood=-98.16
## AIC=200.31 AICc=200.59 BIC=203.97aus_airpassengers %>%
autoplot(Passengers) +
autolayer(fit_stochastic %>% forecast(h = 20),
colour = "#0072B2", level = 95) +
autolayer(fit_deterministic %>% forecast(h = 20),
colour = "#D55E00", alpha = 0.65, level = 95) +
labs(y = "Air passengers (millions)",
title = "Forecasts from trend models")
# 10.5 Dynamic harmonic regression
10.5 Dynamic harmonic regression
aus_cafe <- aus_retail %>%
filter(
Industry == "Cafes, restaurants and takeaway food services",
year(Month) %in% 2004:2018
) %>%
summarise(Turnover = sum(Turnover))
fit <- model(aus_cafe,
`K = 1` = ARIMA(log(Turnover) ~ fourier(K=1) + PDQ(0,0,0)),
`K = 2` = ARIMA(log(Turnover) ~ fourier(K=2) + PDQ(0,0,0)),
`K = 3` = ARIMA(log(Turnover) ~ fourier(K=3) + PDQ(0,0,0)),
`K = 4` = ARIMA(log(Turnover) ~ fourier(K=4) + PDQ(0,0,0)),
`K = 5` = ARIMA(log(Turnover) ~ fourier(K=5) + PDQ(0,0,0)),
`K = 6` = ARIMA(log(Turnover) ~ fourier(K=6) + PDQ(0,0,0))
)
fit %>%
forecast(h = "2 years") %>%
autoplot(aus_cafe, level = 95) +
facet_wrap(vars(.model), ncol = 2) +
guides(colour = "none", fill = "none", level = "none") +
geom_label(
aes(x = yearmonth("2007 Jan"), y = 4250,
label = paste0("AICc = ", format(AICc))),
data = glance(fit)
) +
labs(title= "Total monthly eating-out expenditure",
y="$ billions")
# 10.6 Lagged predictors
10.6 Lagged predictors
insurance %>%
pivot_longer(Quotes:TVadverts) %>%
ggplot(aes(x = Month, y = value)) +
geom_line() +
facet_grid(vars(name), scales = "free_y") +
labs(y = "", title = "Insurance advertising and quotations")
fit <- insurance %>%
# Restrict data so models use same fitting period
mutate(Quotes = c(NA, NA, NA, Quotes[4:40])) %>%
# Estimate models
model(
lag0 = ARIMA(Quotes ~ pdq(d = 0) + TVadverts),
lag1 = ARIMA(Quotes ~ pdq(d = 0) +
TVadverts + lag(TVadverts)),
lag2 = ARIMA(Quotes ~ pdq(d = 0) +
TVadverts + lag(TVadverts) +
lag(TVadverts, 2)),
lag3 = ARIMA(Quotes ~ pdq(d = 0) +
TVadverts + lag(TVadverts) +
lag(TVadverts, 2) + lag(TVadverts, 3))
)
glance(fit)## # A tibble: 4 × 8
## .model sigma2 log_lik AIC AICc BIC ar_roots ma_roots
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <list> <list>
## 1 lag0 0.265 -28.3 66.6 68.3 75.0 <cpl [2]> <cpl [0]>
## 2 lag1 0.209 -24.0 58.1 59.9 66.5 <cpl [1]> <cpl [1]>
## 3 lag2 0.215 -24.0 60.0 62.6 70.2 <cpl [1]> <cpl [1]>
## 4 lag3 0.206 -22.2 60.3 65.0 73.8 <cpl [1]> <cpl [1]>fit_best <- insurance %>%
model(ARIMA(Quotes ~ pdq(d = 0) +
TVadverts + lag(TVadverts)))
report(fit_best)## Series: Quotes
## Model: LM w/ ARIMA(1,0,2) errors
##
## Coefficients:
## ar1 ma1 ma2 TVadverts lag(TVadverts) intercept
## 0.5123 0.9169 0.4591 1.2527 0.1464 2.1554
## s.e. 0.1849 0.2051 0.1895 0.0588 0.0531 0.8595
##
## sigma^2 estimated as 0.2166: log likelihood=-23.94
## AIC=61.88 AICc=65.38 BIC=73.7insurance_future <- new_data(insurance, 20) %>%
mutate(TVadverts = 8)
fit_best %>%
forecast(insurance_future) %>%
autoplot(insurance) +
labs(
y = "Quotes",
title = "Forecast quotes with future advertising set to 8"
)
# 10.7 Exercises
10.7 Exercises
vic_elec_daily <- vic_elec %>%
filter(year(Time) == 2014) %>%
index_by(Date = date(Time)) %>%
summarise(
Demand = sum(Demand)/1e3,
Temperature = max(Temperature),
Holiday = any(Holiday)) %>%
mutate(
Temp2 = I(pmax(Temperature-20,0)),
Day_Type = case_when(
Holiday ~ "Holiday",
wday(Date) %in% 2:6 ~ "Weekday",
TRUE ~ "Weekend"))11.1 Hierarchical and grouped time series 11.1 Hierarchical and grouped time series
tourism <- tsibble::tourism %>%
mutate(State = recode(State,
`New South Wales` = "NSW",
`Northern Territory` = "NT",
`Queensland` = "QLD",
`South Australia` = "SA",
`Tasmania` = "TAS",
`Victoria` = "VIC",
`Western Australia` = "WA"
))
tourism_hts <- tourism %>%
aggregate_key(State / Region, Trips = sum(Trips))
tourism_hts## # A tsibble: 6,800 x 4 [1Q]
## # Key: State, Region [85]
## Quarter State Region Trips
## <qtr> <chr*> <chr*> <dbl>
## 1 1998 Q1 <aggregated> <aggregated> 23182.
## 2 1998 Q2 <aggregated> <aggregated> 20323.
## 3 1998 Q3 <aggregated> <aggregated> 19827.
## 4 1998 Q4 <aggregated> <aggregated> 20830.
## 5 1999 Q1 <aggregated> <aggregated> 22087.
## 6 1999 Q2 <aggregated> <aggregated> 21458.
## 7 1999 Q3 <aggregated> <aggregated> 19914.
## 8 1999 Q4 <aggregated> <aggregated> 20028.
## 9 2000 Q1 <aggregated> <aggregated> 22339.
## 10 2000 Q2 <aggregated> <aggregated> 19941.
## # … with 6,790 more rowstourism_hts %>%
filter(is_aggregated(Region)) %>%
autoplot(Trips) +
labs(y = "Trips ('000)",
title = "Australian tourism: national and states") +
facet_wrap(vars(State), scales = "free_y", ncol = 3) +
theme(legend.position = "none")
prison <- readr::read_csv("https://OTexts.com/fpp3/extrafiles/prison_population.csv") %>%
mutate(Quarter = yearquarter(Date)) %>%
select(-Date) %>%
as_tsibble(key = c(Gender, Legal, State, Indigenous),
index = Quarter) %>%
relocate(Quarter)## Rows: 3072 Columns: 6
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (4): State, Gender, Legal, Indigenous
## dbl (1): Count
## date (1): Date
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.prison_gts <- prison %>%
aggregate_key(Gender * Legal * State, Count = sum(Count)/1e3)
prison_gts %>%
filter(!is_aggregated(Gender), is_aggregated(Legal),
is_aggregated(State)) %>%
autoplot(Count) +
labs(y = "Number of prisoners ('000)")
prison_gts %>%
filter(!is_aggregated(Gender), !is_aggregated(Legal),
!is_aggregated(State)) %>%
mutate(Gender = as.character(Gender)) %>%
ggplot(aes(x = Quarter, y = Count,
group = Gender, colour=Gender)) +
stat_summary(fun = sum, geom = "line") +
labs(title = "Prison population by state and gender",
y = "Number of prisoners ('000)") +
facet_wrap(~ as.character(State),
nrow = 1, scales = "free_y") +
theme(axis.text.x = element_text(angle = 90, hjust = 1))
tourism_full <- tourism %>%
aggregate_key((State/Region) * Purpose, Trips = sum(Trips))11.2 Single level approaches 11.2 Single level approaches
tourism_states <- tourism %>%
aggregate_key(State, Trips = sum(Trips,na.rm=TRUE))
fcasts_state <- tourism_states %>%
filter(!is_aggregated(State)) %>%
model(ets = ETS(Trips)) %>%
forecast()
# Sum bottom-level forecasts to get top-level forecasts
fcasts_national <- fcasts_state %>%
summarise(value = sum(Trips), .mean = mean(value))
tourism_states %>%
model(ets = ETS(Trips)) %>%
reconcile(bu = bottom_up(ets)) %>%
forecast()## # A fable: 144 x 5 [1Q]
## # Key: State, .model [18]
## State .model Quarter Trips .mean
## <chr*> <chr> <qtr> <dist> <dbl>
## 1 ACT ets 2018 Q1 N(701, 7651) 701.
## 2 ACT ets 2018 Q2 N(717, 8032) 717.
## 3 ACT ets 2018 Q3 N(734, 8440) 734.
## 4 ACT ets 2018 Q4 N(750, 8882) 750.
## 5 ACT ets 2019 Q1 N(767, 9368) 767.
## 6 ACT ets 2019 Q2 N(784, 9905) 784.
## 7 ACT ets 2019 Q3 N(800, 10503) 800.
## 8 ACT ets 2019 Q4 N(817, 11171) 817.
## 9 ACT bu 2018 Q1 N(701, 7651) 701.
## 10 ACT bu 2018 Q2 N(717, 8032) 717.
## # … with 134 more rows# data %>%
# aggregate_key() %>%
# model() %>%
# reconcile() %>%
# forecast()11.4 Forecasting Australian domestic tourism 11.4 Forecasting Australian domestic tourism
tourism_full <- tourism %>%
aggregate_key((State/Region) * Purpose, Trips = sum(Trips))
fit <- tourism_full %>%
filter(year(Quarter) <= 2015) %>%
model(base = ETS(Trips)) %>%
reconcile(
bu = bottom_up(base),
ols = min_trace(base, method = "ols"),
mint = min_trace(base, method = "mint_shrink"),
)
fc <- fit %>% forecast(h = "2 years")
fc %>%
filter(is_aggregated(Region), is_aggregated(Purpose)) %>%
autoplot(
tourism_full %>% filter(year(Quarter) >= 2011),
level = NULL
) +
labs(y = "Trips ('000)") +
facet_wrap(vars(State), scales = "free_y")
fc %>%
filter(is_aggregated(State), !is_aggregated(Purpose)) %>%
autoplot(
tourism_full %>% filter(year(Quarter) >= 2011),
level = NULL
) +
labs(y = "Trips ('000)") +
facet_wrap(vars(Purpose), scales = "free_y")
fc %>%
filter(is_aggregated(State), is_aggregated(Purpose)) %>%
accuracy(
data = tourism_full,
measures = list(rmse = RMSE, mase = MASE)
) %>%
group_by(.model) %>%
summarise(rmse = mean(rmse), mase = mean(mase))## # A tibble: 4 × 3
## .model rmse mase
## <chr> <dbl> <dbl>
## 1 base 1721. 1.53
## 2 bu 3070. 3.16
## 3 mint 2158. 2.09
## 4 ols 1804. 1.6311.6 Forecasting Australian prison population 11.6 Forecasting Australian prison population
fit <- prison_gts %>%
filter(year(Quarter) <= 2014) %>%
model(base = ETS(Count)) %>%
reconcile(
bottom_up = bottom_up(base),
MinT = min_trace(base, method = "mint_shrink")
)
fc <- fit %>% forecast(h = 8)
fc %>%
filter(is_aggregated(State), is_aggregated(Gender),
is_aggregated(Legal)) %>%
autoplot(prison_gts, alpha = 0.7, level = 90) +
labs(y = "Number of prisoners ('000)",
title = "Australian prison population (total)")
fc %>%
filter(
.model %in% c("base", "MinT"),
!is_aggregated(State), is_aggregated(Legal),
is_aggregated(Gender)
) %>%
autoplot(
prison_gts %>% filter(year(Quarter) >= 2010),
alpha = 0.7, level = 90
) +
labs(title = "Prison population (by state)",
y = "Number of prisoners ('000)") +
facet_wrap(vars(State), scales = "free_y", ncol = 4) +
theme(axis.text.x = element_text(angle = 90, hjust = 1))
fc %>%
filter(is_aggregated(State), is_aggregated(Gender),
is_aggregated(Legal)) %>%
accuracy(data = prison_gts,
measures = list(mase = MASE,
ss = skill_score(CRPS)
)
) %>%
group_by(.model) %>%
summarise(mase = mean(mase), sspc = mean(ss) * 100)## # A tibble: 3 × 3
## .model mase sspc
## <chr> <dbl> <dbl>
## 1 base 1.72 55.9
## 2 bottom_up 1.84 33.5
## 3 MinT 0.895 76.8